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MATHEMATICS RESEARCH DEVELOPMENTS

STRUCTURAL ANALYSIS

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STRUCTURAL ANALYSIS

MATTHEW L. CAMILLERI EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2010 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Structural analysis / editor, Matthew L. Camilleri. p. cm. Includes bibliographical references and index. ISBN 978-1-61728-481-6 (eBook) 1. Structural analysis (Engineering) I. Camilleri, Matthew L. TA645.S743 2010 624.1'7--dc22 2010015605

Published by Nova Science Publishers, Inc. New York

CONTENTS

Preface vii

Chapter 1 Those Dumb Artists: Amnesiacs, Artists, and other Idiots

1

Dena Shottenkirk and Anjan Chatterjee

Chapter 2 The Structure and Function of Vegetal Ecosystems of Semiarid Areas in Northeastern Mexico

21

P. Rahim Foroughbakhch, Glafiro J. Alanis Flores, Jorge L. Hernández Piñero and Artemio Carrillo Parra

Chapter 3 Structural Analysis of Metal-Oxide Nanostructures 51 Ahsanulhaq Qurashi, M. Faiz and N. Tabet

Chapter 4 A Method for Supporting Efforts to Improve Employee Motivation Using a Covariance Structure

73

Yumiko Taguchi, Daisuke Yatsuzukaand Tsutomu Tabe

Chapter 5 A Block Multifrontal Substructure Method for Solving Linear Algebraic Equation Sets in fe Analysis Software

93

Sergiy Fialko

Chapter 6 Non Linear Structural Analysis. Application for Evaluating Seismic Safety

101

Juan Carlos Vielma, Alex Barbat and Sergio Oller

Index 129

PREFACE Structural analysis comprises the set of physical laws and mathematics

required to study and predict the behavior of structures. This new book gathers and presents current research in the field of structural analysis across a broad spectrum of topics. Discussions in this compilation include: evaluating seismic safety using non-linear structural analysis; a structural analysis of how art is made; the structure and function of vegetal ecosystems in semiarid regions of Northeastern Mexico; using a covariance structural analysis as a method for supporting efforts to improve employee motivation; and a method for solving linear algebraic equation sets in FE analysis software.

The focus of Chapter 1 is " How is it that artists know how to make their work and yet do not know how to explain it?" In other words, how do they both simultaneously know and not know?

True solutions to the problems of arid and semiarid zones throughout the world require a mandatory previous evaluation of the natural resources from the study areas. In Chapter 2, the main approach is driven to the structure and functioning of the vegetal ecosystems which intimately involves various interrelated elements such as flora, cattle, and other fauna, taking also into account management and commercialization of wooden and non wooden products.

A great part of the Mexican republic is located in the world desert belt with approximately 20 million hectares, 18 million located in the northeast of the country, mainly covered by xerophytic shrub vegetation (Rzedowski, 1978, Palacios, 2000). There is no evidence to date about the origin of the different types of shrub populations growing as natural climax vegetation.

Structural analysis plays the key role in understanding the relation between the preparation and morphology and intrinsic properties of metal oxide

Matthew L. Camilleri viii

nanostructures. In Chapter 3 structural analysis of metal oxide nanostructures by variety of fundamental techniques like X-ray diffraction pattern (XRD), Grazing incident X-ray diffraction (GIXRD), Scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), energy dispersive X-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPES) etc will be presented. The detailed crystal structure of In2O3 pyramids will be presented by XRD analysis. For in-situ examination, grazing incidence small angle scattering and diffraction of X-rays (GIXRD) results of ZnO nanostructures will be presented. The morphological description and the examination of the surface of the structure, which includes the analysis of their eventual structure and their size, shape distribution, cross-section from their surface to the substrate, the analysis of the surface and its inhomogenities will be attained by SEM and FESEM analysis. Detailed structural analysis with single crystalline nature of ZnO nanostructures will be presented by TEM equipped with selected area electron diffraction pattern (SAED) and high resolution transmission electron microscope (HRTEM). The surface roughness of vertically aligned ZnO nanorod arrays will be studied by atomic force microscopy (AFM). In order to know the exact chemical composition, stiochiometry and chemical bonding nature of metal-oxide nanostructures, the energy dispersive X-ray spectroscopy (EDX), and X-ray electron spectroscopy (XPES) techniques will be included.

Chapter 4 elucidates a method for showing the priority of factors used to generate motivation in companies, in order to support efforts to improve employee motivation. The authors verify the effectiveness of the method by a questionnaire survey. This study proceeds towards the above-described objectives in the following three steps. Step 1: Discussing the factors which constitute employee motivation in the literature. Step 2: Contriving a method to set the priority of improvement-forming factors by using covariance structure analysis and graphical modeling. Step 3: Verifying the method proposed in this study based on results obtained. The authors had the employees at a restaurant fill out a questionnaire by the method proposed in this study. They confirmed that the improvement priority was effective as a support for improvement.

In Chapter 5, a block substructure multifrontal method for solution of large symmetrical sparse equation sets that appear in finite element problems of structural and solid mechanics is proposed for desktop multicore computers. The analysis of a structure's topology reduces the fill-ins and allows the coupling of equations into blocks. A symmetrical storage scheme for dense matrices and performance optimizations provide a combination of an efficient usage of random-access memory and a high rate of factorization.

Preface ix

Performance-Based Design is currently accepted commonly as the most advanced design and evaluation approach. However, successful application of this procedure depends largely on the ability to accurately estimate the parameters of structural response.

Determination of these parameters requires application of analysis procedures where the main non-linear behavior features (constitutive and geometrical) of structures are included. Chapter 6 presents and discusses these features of non-linear behavior and how they are incorporated in the process of static or dynamic structural analyses. Non-linear analysis leads to determination of significant structural response parameters whenever estimating seismic responses such as ductility, overstrength, response reduction factor and damage thresholds; being these the main response parameters for evaluating the seismic safety of structures. In order to illustrate application of the non-linear procedure being described, a set of concrete-reinforced moment-resisting framed buildings with various numbers of levels was selected. These buildings were designed according to ACI-318 [1] for high and very high level of seismic hazard.

In: Structural Analysis ISBN: 978-1-61668-987-2Editor: M.L. Camilleri, pp. 1-20 © 2010 Nova Science Publishers, Inc.

Chapter 1

THOSE DUMB ARTISTS: AMNESIACS, ARTISTS,AND OTHER IDIOTS

Dena Shottenkirk and Anjan ChatterjeeBrooklyn College, Brooklyn, New York

Amnesiacs, Artists, and Other Idiots

Henry Molaison, aged eighty-two, died at the end of 2008, and just after noonon exactly the first anniversary of his death, December 2, 2009, scientists beganslicing his brain into 2,500 tissue samples. Known primarily in his lifetime as onlyH.M., he left his brain to science so that it could be dissected and digitally mapped– a gift much beloved by many scientists. An amnesiac in life, H.M. first rose toprominence in 1962 when Dr. Brenda Milner, a pioneer in the field ofneuropsychology, demonstrated that though H.M. was severely amnesic and couldnot remember past activities, he could nevertheless learn certain habits. Theexperiment involved the now famous mirror drawing, which tests hand-eyecoordination, and can be learned over a number of days. Though H.M. had noconscious memory of having had done it before, his performance improved: helearned how to trace the image in the mirror reflection. Amnesiacs do well onmirror drawing, which is a kind of perceptual skill that in turn is a kind of motorskill – a type of skill dependent on brain structures distinct from declarativememory e.g., that memory system that allows us to consciously recall an event orfact. As an amnesiac, his declarative memory system – the part of his brain thatcould consciously remember – had been damaged. But as the 1962 study of H.M.

Dena Shottenkirk and Anjan Chatterjee2

showed that he could increase his hand-eye coordination on the mirror drawing,even though he was unable to have any memory of having practiced this task, italso showed that there was another memory system in the brain. In other words,he could remember to do something but not recall his doing of it. He didn’t knowthat he knew.

Artists are often stereotyped as inarticulate – connected to their muse but notto their grammar. The image of the inarticulate artist, caught in the bowels ofemotionalism and compulsions, finds a home not only in the shorthand ofHollywood thinking and the therefore easily portrayed van Gogh, Pollock, orPicasso, but also in the more nuanced experiences of art writers, curators, andmuseum directors who have had the often repeated experience of listening to anartist’s inarticulate and overly vague description of his or her work or thecompensating opposite of a neatly packaged and overly slick byline-typeexplanation gleaned from memorizing what others have said about the work.These experiences buttress the opinion often heard that artists simply don’t knowwhat they are doing; that is best left up to others to articulate. Hence, curators andcritics still have jobs.

There is though a continuing belief that artists ought to be able to discuss theirown work and artists’ training often involves a requirement for the art student towrite about his or her work, especially in professional graduate programs. Thedifficulty of this is the cause of many art department meetings as well as thesource of many jokes, though art educators tend to not dismiss art students’inabilities with the same wave of the hand often seen in the professional artworld.Instead, art educators believe the inability is remedial, and to a certain extent thatbelief is carried over into the professional world that demands such things asinterviews and artists’ statements. While this is clearly inconsistent (this demandto articulate while simultaneously dismissing artists for being inarticulate), that isnot the focus of the current investigation.

Rather, the interest is in the differing abilities relative to an artist being able todiscuss others’ work and being relatively unable to discuss his or her own work.Returning to the case of academic training, it is routinely noted that the very sameart students who have difficulty writing about their own art are quite often veryarticulate about other art students’ work. The contrast between the ability todiscuss one’s own art and discuss the work of others is sometimes noted byacademics and teachers, as students participate in group critiques of otherstudents’ work on a regular and successful basis without the stammering,obfuscation, or packaged byline approach that often infuses discussions of theirown work. It is often just passed off as a difference resulting from a lack ofpractice, and hence art students are encouraged to write more about their own

Those Dumb Artists: Amnesiacs, Artists, and other Idiots 3

work. But why would art students have less practice in discussing their own workthan in discussing others’ work? An art student has surely spent more timeinvolved in his or her own work and has had more thoughts directed to thatprocess than thoughts expressed in group critiques of other students’ work. Theexplanation must be elsewhere.

The challenge involved in artists discussing their own work travels beyondthe ivory tower, as well. In similar fashion, professional artists routinely discusswork they’ve seen in galleries and can do so with great fluency often incontradistinction to the explanation they might give of their own work, except thatdifferently from students, they often have gravitated to the overly-practiced bylineexplanation. Given that there is a bifurcation between the way an artist speaks ofhis or her own work and the way an artist is able to speak of others’ work – in thatway functioning more like a critic – the question is why. The fact that there is adifference is a point upon which practitioners in the field readily agree, but thedisjunct between the inability of an artist to discuss his or her own work and thatsame artist’s ability to discuss the work of others is rarely highlighted or analyzed.

That is the focus of this investigation. How is it that artists know how to maketheir work and yet do not know how to explain it? In other words, how do theyboth simultaneously know and not know?

It is important to note that some psychological explanations are implausible.If one is tempted to adopt a traditional psychoanalytic explanation to the differingsemantic abilities, there are in turn two potential candidates: artists might beunwilling to brag or they might be “too close” to the phenomenon. The firsthypothesis seems fairly easy to dismiss in most cases as the idea that artists arelacking in strong egos and self-confidence is a hypothesis probably not worthtesting. “As meek as an artist” isn’t a common truism; that’s reserved for a mouse.Art is an activity that often requires not only the offering of one’s ownimpoverishment, at least temporarily but often long-term, but the sacrifice of astable family life as well – all in the name of expressing one’s inner self. Clearlyvaluing one’s “inner self” higher than one’s own physical well-being or often thewell being of those around one does not speak of humility or self-abnegation. Thepremises are weak in this argument.

A related notion though could be put forth that artists are “too close” to theirown work and therefore unable to get the distance needed to discuss it. While it istrue that “closeness” to something can prevent needed objectivity – the oftrepeated metaphor of not seeing the forest for the trees – an analysis of what itmeans to be “too close” is worth a digression. For example, a conflict at workwith a fellow colleague can have this flavor as can a conflict with a familymember or a loved one. In these kinds of circumstances it is difficult if not


impossible to get the bigger picture and to explicate the situation fairly. One’sown version – or one’s own vulnerabilities – is often all one sees.

It can be easily argued that this experience tends to happen in an arena whereone’s own needs are in competition with others’ needs and heeding a call toobjectivity would potentially endanger one’s own position. The protective part ofthe brain – that old part that institutes a flight or fight response – can shut downthe processes that allow for a more reflective and more nuanced objectivity. Inthis instance the person is “too close” but it is too close to one’s own survival thatis at issue for the brain. Objectivity in this immediate instance is hardly in one’sbest interests, or at least short-term best interests. It is thus a self- preservation thatprevents objectivity.1

But is art in this kind of category? This idea would have some credence if theartist viewed his or her work as an external threat and articulating that work wouldresult in some sort of personal demise. Again, confirmation or disconfirmation ofthis would be an empirical matter for psychology researchers, but the notion thatone’s own art could be a source of threat similar to an external threat lacksimmediate plausibility.

A Structural Analysis of Knowing

An alternative explanation and one embraced here would be a neurologicalone: to be “too close” to the information can be explained through a structuralanalysis of the heterogeneous functions of the brain’s memory systems. This istherefore a structural analysis of how art is made, or more precisely, how art isthought.

It is the “thought” part that is essential. An explication of the process ofmaking art – the phenomenon of the creative act, as it were – has elided manywriters and philosophers who have been summoned to the challenge ofarticulating exactly what it is that the artist is doing and how the creative actcomes to be. The process at first glance though seems relatively simple. An artistbegins to work. Brushes and paints are set up (limiting the example to a visualartist – a conventional painter), the size of canvas has been decided upon,constructed, and primed; the process begins.

But the process had already begun long before that. This physical act is notthe beginning. The artist has already engaged in a long period of training, as wellas having some existing notion of what is to be created in the present case. It is

1 Thanks to Dr. Deborah Munczek, clinical psychologist, for her discussion regarding this.


normally only the second of these facts that are focused on when discussing theso-called mystery of the creative act. But the first part is perhaps more importantbecause that embeds the roles of memory and habit formation, essentialcomponents for creative activity. Together, these two antecedent facts are at theheart of this paper – an articulation of the mental processes behind an artist’screation.

Two Memory Systems

The beginning then is fairly straight-forward: it is not the singular fact thatartists are odd, but the more general fact that the human brain is extraordinarilycomplex. This is especially true when it comes to learning and memory systems.Neuroscience and neuropsychology have developed over the course of the lastfew decades a systematic view of the brain’s memory systems, in which there arecleaved two basic systems: the declarative and the nondeclarative. Thesecorrespond to certain physical regions of the brain, with each of these regionsfurther subdivided both in terms of the physiological structures and in terms offunctional roles. The declarative memory system is located in an interactionbetween the large physiological regions identified as the medial-temporal/diencephalic region and parts of the neocortical region – that part of thehuman brain other mammals would be jealous of if only they knew. Thenondeclarative memory system resides in the basal ganglia, which is located deepwithin the hemispheres of the brain and the cerebellum, which itself is directlyabove the brain stem. Declarative memory encompasses both semantic memory,which is the ability to name and identify third-person objects and events, andepisodic memory, which is those memories each of us have contextualized in timeand therefore more first-person e.g., what I ate for dinner last night, what I waswearing when I got married, etc. Episodic memory involves participation of brainsystems in addition to those that support the semantic, particularly the amygdala,which registers emotional events – obviously important in first-person memories;the episodic memory system is also that part of the brain that encodes previousexperiences of third-person objects (which is why those childhood hills seemed sovery huge). Nondeclarative memory, on the other hand, encompasses skilllearning (also thought of a procedural memory e.g., swinging a golf club, playinga piano, etc.), repetition priming (the larger category also including perceptuallearning, of which mirror writing is an instance), and classical conditioning (oftenthought of as stimulus-response kind of pavlovian learning and also sometimesinvolving the amygdala). These two memory systems also divide along the


explicit/implicit line, with declarative obviously being explicit memory andnondeclarative memory, which is very often implicit. The relationship to languageis critical, as anything implicit usually does not have access to language (or theother way around) since only some explicit i.e., declarative, things do have suchaccess.

The argument we are presenting is that making art and thinking about one’sown art involves the use of memory systems not activated in the alternatecircumstance of critically discussing another artist’s work. In the latter, the objectis accessed similarly (though not quite identically) to other third-person objects,such as a table, a fork, or a mountain. Making art and therefore retrieving thosememories of making one’s own art involves other parts of the brain that might notbe as accessible to semantic and declarative memory and hence to language.

Amnesiacs and Medial Temporal Lobe Damage

The analogy brought to bear by amnesiacs is to be considered initially.Amnesiacs suffer damage to the medial temporal lobe or midline diencephalon,which is the locale for episodic declarative memory, but they do have access tonondeclarative memory. Working from analogy, if we begin with the establishedfact that amnesiacs do not have access to declarative episodic memory but canaccess nondeclarative memory, it is then possible to conceptually frame the issuein a way that divides the ability to remember nondeclarative things from theability to know – declaratively – that one is remembering something, either a firstperson or a third person fact. This might then parallel the disjunct between theartist's ability to make something and the ability to know and articulate what onehas made, if it can be shown that functions operating in the nondeclarativesystems need not fully translate themselves into the declarative mode.

Amnesiacs also show other kinds of nondeclarative memory functions, suchas the ability to prime. Priming is when they have been shown a word or imageand then at a later date tested on that knowledge, for example, when they aredirected to use a particular word to form the first word that comes to mind, andthough they have been given that association in the past and are therefore biasedby previous exposures, they do not declaratively remember though they implicitlyremember. In this, the point is that "memory" in the ordinary usage of the term isabsent: the patients cannot remember the word recently given to them. But whenasked for quick associations they perform more adequately, indicating that theyare drawing on other kinds of memory storage systems.


This establishes the following things. As amnesiacs with lesions in the medialtemporal lobe cannot remember that they have increased their learning of mirrordrawing, this indicates that others can in theory also draw from nondeclarativememory, establishing in turn that this knowledge is stored in a part of the brainother than the medial temporal lobe e.g., in a part other than the episodicdeclarative memory system. In theory then it is possible to know (using the wordin the way that means implicitly knowing) some kind of perceptual or motorprocesses but not have linguistic access to it. So while the medial temporal lobedoes in the usual case store accessible memories of this kind of visual learningthat can then be reformulated as semantic thought in a normal person, the medialtemporal lobe obviously doesn't control all learning per se. Perceptual and motorlearning is done somewhere else, - (or at least it can in some instances be donesomewhere else, as it is possible that only in the instance where the medialtemporal lobe is damaged does the nondeclarative memory compensate) – and themind is able to retrieve that learning and act on it, but it cannot easily share it withothers; it cannot be translated into language as it is no longer accessible to thedeclarative part of the brain.

Like perceptual memory, procedural memory too can continue to function inpatients who have undergone surgical removal of the medial temporal lobes(Budson, 696). Procedural memory survives independent of episodic memory andsemantic memory as demonstrated not only by amnesiacs who suffer fromdamage to the medial temporal lobes (which includes both the hippocampus andparahippocampus), but it is also the case that procedural memory functionssurvive in patients with alcoholic Korsakoff's syndrome that are amnesiac becauseof damage to the diencephalon. These procedural skills, exemplified in suchactivities as playing music or swinging a golf club, are dependent upon both thecerebellum region and the supplementary motor area along with the striatum (apart of the basal ganglia), thus like mirror-writing and priming. These areas areactive as a new task is learned and thus it governs learning behavioral, cognitiveskills and algorithms at an unconscious level. Hence, both perceptual memory andthe procedural memory, each part of the nondeclarative memory systems, cancontinue to function in people without access to declarative memories.

This establishes the fact that at least these nondeclarative memory systems areable to function in the absence of declarative memory function. But it is importantto remember that nondeclarative memory systems do not serve the same purposesthat declarative systems serve. As Larry Squire, a pioneer in memory research,stated regarding the nondeclarative memory system, "It is dispositional and isexpressed through performance rather than recollection. Nondeclarative forms ofmemory occur as modifications within specialized performance systems. The


memories are revealed through reactivation of the systems within which thelearning originally occurred"( Squire, 173-4).

If it is true that the memories are revealed through reactivation of the systemsand it is clear that in amnesiacs there is no functioning memory in the episodicdeclarative area, then it seems to be the case that in healthy persons, who are ableto both do the assigned tasks and know that they have done the assigned tasks,must be storing two different sets of related memories; it indicates that the healthyperson has both the nondeclarative and the declarative memory systems operatingfor this particular task. In other words, for a healthy person the memory is storedin two places – one for their own private use, so to speak, that can be called uponto actually do the task and is therefore is activated when they need to put theknowledge to use (on analogy with the amnesia patient) and one that is in usewhen the knowledge of the task is shared, literally with others. The latter instancewould be needed for those times when the activity is codified in language andarticulated to others. But it wouldn't be needed otherwise.

Damage to the Basal Ganglia

Where patients with damage to the medial temporal lobe reveal the workingsof implicit memory, the obverse situation is revealed in studies done with subjectssuffering from Parkinson’s and Huntington’s. These patients suffer from damageto the organic parts of the brain that affect the nondeclarative memory system, i.e.,the basal ganglia. The separate input areas of the basal ganglia most relevant forhabit learning are the caudate nucleus and the putamen, along with the globuspallidus and the claustrum. By the late 1970s neurologists had refined theterminology and the basal ganglia was divided into the ventral striatum and thedorsal striatum. Of course this part of the brain like all parts of the brain does notoperate in isolation and the basal ganglia receives inputs from all regions of thecerebral cortex. Spines on neurons conduct, via neurotransmitters, information toother neurons and hence other regions of the brain. Along with the striatum, thecerebellum, sitting next to the brain stem, is involved in procedural memoryformation. The neural networks that connect these sites to each other and to thecognitive apparatus found in the neocortex are critical for the planning andmonitoring of behavior.

The basal ganglia was long associated with motor control, and diseases of thebasal ganglia such as Parkinson’s and Huntington’s highlight these functions. Butthe basal ganglia is now thought to be associated with certain kinds of learning aswell. As nerve cells die as a result of each of these diseases, it is possible to


monitor what corresponding functions are likewise diminished, thus establishingthe functional roles of specific parts of the brain. While motor deficits are mostobvious in both of these diseases, nonmotor functions including cognitive deficitsalso exist, indicating a larger role for the basal ganglia in memory functions.(Packard, 2002, 575) Thus recent research has also shown that a part of the basalganglia, specifically the dorsal striatum, is involved in learning and memory, inparticular the kind of learning associated with stimulus-response.

A fascinating function of basal ganglia in habit formation is the role ofprobabilistic learning. Various tasks have tried to gauge the role of the basalganglia in probability reasoning, the most famous of which is the weatherprediction game (established in the mid 1990s at Rutgers University). In thisgame, four tarot type cards each with a unique geometric pattern (e.g., squares,diamonds, circles, or triangles) are presented to the subjects. A random selectionof the cards would then be given to the participants, sometimes for example,consisting only of one card – a square – or another card – a diamond – or bothcards, etc. On each trial, subjects are asked to see the pattern and then predict theweather. They of course believed (cognitively) that they are randomly guessing atthe weather, but in truth there was a probability correlation between some of thecards and rain/shine predictions. After 50 trials, healthy subjects improved theirprediction capability without consciously knowing that they had done so.Amnesiac patients, who have damage in the medial temporal lobe that are criticalfor declarative memory, also learn the task normally within 50 trials, whereasneither Parkinson nor Huntington patients do. The results of this test againestablish that the basal ganglia is the seat of the nondeclarative memory, whereasepisodic declarative memory is found in the medial temporal lobes.

Basal ganglia mnemonic processing also involves skills associated withlearning a visuomotor sequence. Tests developed for this have often beenvariations on a timed finger tapping reaction, such as when the subjects view aseries of asterisks on the computer and then are asked to press the key directlybelow each asterisk as it appears. Though the asterisks are in an ordered sequence,the subjects are not told this and yet their reaction time will improve; again,demonstrating that we know things we don’t know we know. The brain caninductively decipher the pattern, and react appropriately to that pattern, and yetthe conscious, declarative part of the brain is unaware of it. Again, amnesiacpatients, who suffer from damage to the medial temporal lobe/hippocampalsystem, show no diminished ability to perform this task, also therebydemonstrating that the part of the brain that memorizes inductive patterns and usesthose inductive patterns in habit formation is in tact.


Animal studies have been valuable in extracting data relevant for decipheringthe different memory systems. Rats were used in various studies beginning in the1960s to test the mnemonic functions of the dorsal striatum section of the basalganglia in habit learning, wherein two eight-arm radial maze tasks were used inwhat was called a “win-stay” version. This test was derived from an older testcalled the “win-shift” task, which required rats to remember what arms they’dpreviously visited and to not revisit them; in other words, rats received food whenthey visited only an arm of the maze that had not been visited before. This testedfor spatial memory and/or a kind of cognitive mapping strategy. The newer test,e.g., the “win-stay”, was developed in the late 1980s, and also used an eight-armmaze but in this instance some of the arms were illuminated and some were not,with the rats rewarded only when they chose an illuminated arm. This tests forvisual discrimination and the acquisition of an stimulus-response (S-R) habit.Brain lesions in the dorsal striatal part of the brain (i.e., the basal ganglia regionthat controls habit formation) allowed the rats success at the win-shift task (i.e.,the task that tested for spatial memory and/or a cognitive mapping strategy), butimpaired their ability to succeed in the win-stay task, which tested for theacquisition of an S-R habit. The inverse held for rats with lesions in thehippocampal system (i.e., the system that controls for cognitive memoryfunctions): they were able to succeed at the win-stay task but not the task thattested cognitive mapping strategy, e.g., the win-stay task. In other words, lesionsin the hippocampal region impair cognitive functions whereas lesions in the dorsalstriatal region of the basal ganglia impair habit formation. The rats with lesions inthe hippocampal system would, like H.M., know but not know they knew (ifindeed rats can be said to know at all…). The question of course is whether or notartists also find themselves in that position.

The Two Memory Systems: War and Peace

Of course the two parts of the brain – the medial temporal lobe and the basalganglia – are not wholly cordoned off from one another. The relationship betweenthe various memory systems has also been a source of recent research and again ispertinent to present purposes. Several different neural pathways have been foundtravelling between the basal ganglia and the medial temporal lobe, such as outputprojections to frontal cortical regions, which then in turn project to the medialtemporal lobe (Packard 2002, 583).

The details of the relationship between the various parts of the brain are ofcourse revealed in knowledge of the molecular level. The basal ganglia receives


input in the spiny neurons found in the striatum and these spiny neurons receiveconvergent signals from various areas of the cerebral cortex. Using dopamine as aneurotransmitter, these spiny neurons are thought to recognize patterns of activity.Upon registering this context, the spiny neurons discharge their dopamine whichis thought to disinhibit thalamocortical loops to initiate sustained activity inclusters of frontal cortical neurons (Houk, Davis, Beiser, 1995). In this way theoriginally detected contexts would be recognized as working memories. Repeatedactivation gives an increase in the efficiency of the synaptic transmission and iscalled long-term potentiation (LTP). This is thought to construct memoryformation, and recent research has been done on exactly what kinds oftransmitters are necessary for this process. While the cellular-level research willultimately answer many questions as to the precise mechanisms that underliememory formation, the important fact for present concerns is that the existence ofseparate memory systems is now widely accepted. Questions currently underinvestigation are how these memory systems interact with one another in thefunctional construction and retrieval of memories.

Neuorimaging studies have corroborated the notion that memory is not asingular entity nor accomplished through a single organ, and current focus forstudy is the cellular mechanisms of the system as well as the relationship betweenthe larger parts. Research involving patients with brain lesions, and in particularH.M., began the field of study, but it is neuroimaging – both positron emissiontomography (PET) and functional magnetic resonance imaging (fMRI) – that ismore reliable as the behavior of memory-impaired patients with brain lesionswould demonstrate not the function of the damaged brain isolated from the rest ofthe brain but also a brain that has compensated for that damage (Gabrieli, 1998).Some of these neuorimaging studies are particularly important for presentpurposes. In the mid and late 1990s, studies revealed that the caudate nucleus partof the basal ganglia was activated during a serial reaction-time task, but this partof the brain was not activated when the subjects were told beforehand and couldtherefore explicitly anticipate the stimulus (Packard & Knowlton, 2002, 578 –referring to Doyon et al. 1996): need to find this original source to double check)This is important as it points to the fact that explicitly knowing something is ininverse relationship to implicit knowledge and habit formation.

The relationship between the two main systems of memory – the basalganglia and the medial temporal lobe system (sometimes also referred to as thehippocampal system) seem to both exhibit competition and cooperation. In sometests, there is evidence that the beginning of learning shows a simultaneousactivation in both regions, with the hippocampal system activating the learningthat initially controls behavior, but that that activity then shifts to the basal ganglia


region, particularly the caudate memory system, which then controls for a slowerprocess of S-P habit formation (Packard & Knowlton, 2002, 579).

But scientists have inferred from other studies that there are instances wherethe memory systems compete. D.F. Sherry and D.L. Schacter, in “The Evolutionof Multiple Memory Systems”, written in 1987, have given an evolutionary storyhypothesizing that the inability of one memory system to successful solveproblems and adapt to situations led to the development of multiple andheterogeneous systems. The fact that a brain suffering from a lesion in one partcan experience enhanced and compensatory learning in another part of the brain issometimes evidenced as proof for this theory. Experiments using artificiallyinduced neurotransmitters (in the form of pharmacologically enhanced thinking)have proven that the different memory systems can be both enhanced anddiminished, biasing animals toward one or the other. But as some researchers havenoted, “…in some cases of caudate-dependent sequence learning, explicitknowledge of the sequence can impair acquisition as measured by reaction time,which suggests that effortful retrieval of explicit knowledge can interfere with theperformance of the implicitly learned sequence” (Packard & Knowlton, 2002,582). Poldrack (2003) has noted that activation in the caudate nucleus and medialtemporal lobe is negatively correlated within subjects, a point crucial for presentpurposes.

It is conceivable that the making of art does not have to be data that is storedin both memory systems. But as declarative memory is both semantic andepisodic, (the latter relies on some of the same structures as does the semanticmemory system plus the frontal lobe) then the claim that speaking about one'swork is inaccessible to declarative memory must then also entail a claim thatmemory about (or conscious knowledge of) an artist's own work is not stored ineither the semantic nor the episodic memory system. This would be the logicalconclusion but it is counter-intuitive. We intuitively think of making artwork asjust that kind of activity that one locates as those "personal experiences framed inour own context" i.e. episodic. If that is true and if making one's art is just likeremembering other first person events, then it would not make sense to claim thatthe experience of making one's own work would be less accessible to verbalizingthan would be others' artwork. In other words, my art seems like my art, whichcan't be that different from my birthday. Or is it?

It makes sense to conceptualize this as a system of interdependent signals. Itis known that memory systems operate in parallel and independently, sometimesworking parallel to support one another. In these instances they seem to replicateeach other and reinforce a memory as when a fear of heights (in the


nondeclarative memory system) is coupled with the specific memory of falling offthe porch as a small child (in the declarative memory system).

But there are also competing situations where multiple systems are operatingsuch as when, to again quote Squire, "…humans acquire a difficult habit learningtask, structures important for habit learning and structure important formemorizing (i.e., declarative memory) can appear to compete for control ofperformance" (Squire 174). Recent research by Poldrack and Packard points tosome instance of competition among systems, but in the example provided earlier,in the early phase of learning the difficult task, fMRI showed the activity in themedial temporal lobe, a pattern found if subjects were attempting to memorize thetask, but as their performance improved, the fMRI activity decreased in the medialtemporal lobe but the activity in the neostriatum increased. In other words, theactivity increased in the area of the brain that controls habit formation. Thus, thesetwo brain functions related inversely in the activity of learning a difficult newtask. This could indicate, as Squire would want to argue, that the two parts of thebrain are competing with one another. But it could also indicate a kind ofevolutionarily devised cooperation among mutually symbiotic systems, as it couldthough also be the case that both functions are necessary in order to satisfactorilylearn the new task – both memorizing the data in the immediate short-run andcommitting that data to a habit formed system, for purposes of easier retention. Inthis interpretation the system would be set up to optimize the different kinds ofmemory dependent on the task at hand. In that scenario, the memories are notprimarily stored in the declarative system, however episodic the origins of thosememories might be. In other words, it would not be just like one’s birthday.

Whether the system is one of competition or cooperation, it is still the casethat the different memory systems process different activities and store the datadifferently. It also needs to be noted, somewhat parenthetically, that it seems to bethe case that these memory systems are not completely physiologically mutuallyexclusive. Rather, these systems are interconnected and overlap in places. Sowhen we speak of different memory systems our language is provisional at leastin part. These should not be taken as isolated systems necessarily blind to eachothers’ workings.

It is also worth noting that research comparing the ways humans learn patterndiscrimination differently from monkeys shows that the same thing can be learneddifferently: the monkeys seem to learn it by depending on the temporal lobe –neostraital pathway i.e., the procedural memory system, whereas humans learn itas a kind of memorization using the medial temporal lobe i.e., the semanticmemory system. But, Squire notes, "To achieve a two-choice discrimination taskfor humans that is an acquired skill, and in the way that monkeys learn the pattern


discrimination task, one might ask humans to learn to discriminate between thepaintings of a master and the paintings of a talented forger" (Squire 174).Procedural memory is important in the kind of feedback and gradual learningsystem that seems reflective of the process of learning how to distinguish thesubtleties that differentiate the original painting from the fake. It seems clear thatprocedural memory would also be involved in functioning as a critic (in additionto the obvious main role of semantic memory), in that critics might learn patterndiscrimination in the experience of learning to see the difference between the fakeand the real, but it might very well also be true that that critic would be unable toexplain to someone else exactly why – in every detail – the fake wasdistinguishable from the real.

The Amygdala’s Role in Emotional Memories: The Gist

None of this discussion though has touched upon a very important aspect ofthe brain: the amygdala. Nominally a part of the declarative memory system, theamygdala is near the hippocampal formation, and stores highly emotionalmemories, such as instances of fear, and matures in small children sooner than thehippocampal memory system (LeDoux, 1996, 202), which explains why childrencannot retain explicit memories and yet will have phobias that result from earlychildhood events. A stimulus that reminds the brain of the earlier experience willthus be experienced both in the hippocampal memory system - in declarativememory – and in the implicit memory system controlled by the amygdale. In thisinstance the two memory systems meet and the product is the profound andemotionally intense re-experience.

Recent studies have demonstrated that amygdala damage impairs the brain’sability to retain the emotional gist of an event (Adolphs, Tranel & Buchanan,2005, 512). Patients with damage to their amygdala showed no enhanced memorywhen experiencing something under an emotionally encoding experience (e.g., apicture of parents in a car travelling down the road versus the same picture withthe explanation that they are on the way to retrieve the remains of their childrenwho died in a plane crash). In subjects without brain damage, their memory of thetraumatic photograph would be enhanced as that memory would have stored itselfin the amygdala. In other words, in healthy subjects, the experience of viewing aphotograph of parents on the way to retrieve the remains of their dead childrenwould be startling and horrifying enough to “register” on their brains: theirmemories would be seared into them. It is probably what Plato was trying toarticulate when he gave the metaphor of a wax imprint for memory; certain things


leave their mark. But this is not the case with in people with damage to theiramygdala.

Furthermore, these memories when stored in the amygdala are encodedprimarily for their “gist” – their overall feel, the generalized emotional responsethat they elicit, rather than for the details of the event. This is a crucial point.Subjects with amygdala damage showed worse gist memory under emotionallyencoding experiences rather than under neutral encoding experiences. This resultwas not exemplified with patients who had medial temporal lobe damage,including to the hippocampus, but excluding the amygdala. This again confirmedthat the amygdala is the locus of the generalized gist of an emotional experience.In addition, the amygdala can affect both medial temporal lobe structures duringthe encoding of a memory as well as participating in fear conditioningindependently of the hippocampus. There is also evidence that it has a limited rolein declarative memory (Gabrieli, 1998, 92), and in its relationship to thehippocampus it is bidirectional as it both influences and is influenced by thehippocampus, thus making the amygdala crucial in its role in various memorysystems (Adolphs, Tranel & Buchanan, 2005, 516).

Memory is in a labile state when it is first acquired. Neurotransmitters, suchas acetylcholine, dopamine and glutamate, conduct the synaptic and cellularmechanisms, insuring either a continuation of the signals (long-term potentiation -LTP) or long-term depression – LTD. These “up” or “down” signals convey notonly discrete bits of information that determine whether the stimulus is continuedor not, they create patterns as well. Thus the amygdala participates in bothimplicit memory formation via fear conditioning and explicit memory formationvia aversive reaction to remembered negative stimuli.

To Know Yet Not Know

Two things are worth noting. The already obvious point that emotions canembed experience in a way a neutral context cannot, and that a memory musttransition from its initially coded state to a more permanent one. The latter can beaccomplished in two distinct ways: either through time or through sleep. “It cameto me in a dream…” is perhaps more true than we used to think.

“I dream of painting and then I paint my dream”, Van Gogh was quoted assaying. He was also quoted as saying, “Poetry surrounds us everywhere, butputting it on paper is, alas, not so easy as looking at it.” It is easy, when viewingan artwork, to describe the lines, the colors, the shapes, the objects depicted. VanGogh for example did quite a bit of that. It is much harder to encapsulate and


translate the meaning – to put down the poetry. This is the function of a critic, andthe task is quite difficult. When we look at a van Gogh what are we taking awayfrom that experience? What does a van Gogh mean?

People are always quite moved when they stand in from of a van Gogh. It isnot a cooling experience. It is about a naked, unadorned reality – thetempestuousness of everyday perceptual fact that in turn seems mildly generousand decorative but is, with a mere slight of one’s head – like those hologram cardsthat switch between Elvis sequined and Elvis naked – immediately destabilized,and somewhat threatening in its psychedelic unfolding of the deeper reality.Emotions broiling, thoughts unhealed. Life blazed for van Gogh, heeded by thecall to its immanent decay. For him, it was always a hot fire and reality wasalways bare.

It is the job of philosophy to tell us what’s meaningful and how exactly it isthat it becomes meaningful. Philosophy breaks up into its segregated disciplinesas it “cleaves the animal at the joints” (to paraphrase Aristotle): logic tells usabout the syntax of our reasoning, epistemology about how we absorb knowledge,metaphysics tells us about the reality that is absorbed before it is absorbed. Andaesthetics is supposed to tell us something about art.

But our recent understanding of the brain tells us that we don’t always tellourselves everything. Language constructs only those things the declarative brainhas access to. And the declarative brain is not the know-it-all it pretends to be.Our reality is concatenated (to steal a word from Kant) – it is pieced together frombits of data that are formed in the process of absorption. As the data enters us weprocess it in ways that fundamentally constructs the form the data now takes forus – unprocessed material is unformed material. In a fundamental way, it is non-existent. At least, again to steal from Kant, phenomenologically. Both Kant andNelson Goodman were correct about this. The important fact now available for usis that much of what we concatenate – much of what our brain has processed andis now using – is unavailable for the declarative, language part of our brains. Andyet we can be said to still “know” it. Trauma victims, for example, are not able tonecessarily linguistically access the source of their trauma or even to recall itconsciously, yet they “know” it. Our knowledge of how to ride a bike, or swing agolf club, is procedural knowledge held in the nondeclarative memory system andwhile we can be said to “know” it we cannot access that informationlinguistically. We need to show it to someone, not tell someone. And if quizzedon this knowledge and pressured into explaining it, we will seem to not know. Wewill seem stupid.

A related point was much of Wittgenstein’s thesis in The PhilosophicalInvestigations: inductive knowledge is powerful yet often without explicit,


linguistic guiding posts. Different parts of the brain absorb data differently, use itdifferently, and recall it differently. The nondeclarative memory system isdispositional, and we use it when we are embedded in that particular task, often aphysical task.

Art is an activity, in part a mental one and in part a physical activity, one thatis repeated over and over, until the artist develops a practice. In fact, that word“practice” is often used by artists in referring to their own art. “My practice”embeds within it one’s habitual use of not only images and materials, but one’sbasic intent: the meaning behind one’s work. “This is what I do; this is mypractice.” This notion includes the meaning behind the art – the content of the art– of which one is almost peripherally aware, a tangential force that thoughmotivates the production of art. Like the “gist” of an emotion known only by theamygdala, an artist’s practice is that intentional and dispositional force that is notamenable to detailed description, at least by the artist. It is a “gist”; a deeply feltperspective of the world, a profoundly remembered attitude of what it means to bethis distinct, physically embedded individual.

And if it is the case, as noted earlier (cf. Packard & Knowlton, 2002, 582),that explicit knowledge of an instance of caudate-dependent sequence learningcan impair the acquisition of that caudate-dependent (i.e., implicit) knowledge,then there is very good reason for artists not articulating the detailed explanationof their work. If in fact it is true that there’s an inverse relationship betweencertain kinds of explicit knowledge and implicit knowledge such that “…effortfulretrieval of explicit knowledge can interfere with the performance of theimplicitly learned…” task, then the demand for the dominance of declarativeretrieval needs to be rethought. The argument would be that an artist articulatingthe meaning behind his or her own work would be similar to the experiments withsubjects and the weather prediction task, e.g., there seems to be competitionbetween the caudate section of the basal ganglia and the medial temporal lobememory system. The recent fMRI study (cf. Poldrack et al. 1999) that shows anegative correlation between the activation in the declarative memory system (e.g,medial temporal lobe) and the nondeclarative memory system (e.g., the basalganglia) points not only to the problem artists have in articulating their own workbut quite exactly to a claim for not demanding that.

Artists know but don’t always know that they know and furthermore knowthat if they know they will no longer be able to have that fluid access to knowingwithout knowing. That can be parsed as follows. Artists often implicitly knowthings without explicitly knowing them, and furthermore implicitly know that ifthey explicitly know they will no longer be able to have access to that fluid part ofthe brain that implicitly knows. The medial temporal lobe will crush the basal


ganglia in triumphant evolutionary zeal. The creative role of the basal ganglia –the role of procedural memory – would be undermined if it were fully translatedinto the declarative part of the brain. In other words, to gain explicit knowledge ofsomething is, to some extent and in some situations, to loose it.

This though would be viewed differently from the point of view of the critic,as there is no artistic procedural practice and no originating amygdala “gist”.Obviously the declarative memory system is primarily involved in the critic’sprocess, and would involve not only the semantic but the other declarativememory system, e.g., episodic, as the critic would also bring in some of his or herown personal associations. But the semantic part of declarative memory would bethe main source upon which the critic would rely as it encodes third personobjects with designated definitions; art only differs from other human artifacts inthat a symbol system has been embedded such that understanding the artworkinvolves negotiating the relationship between the sign and the thing signified. Butthis language – again, as Wittgenstein was correct to point out – is socially arrivedat, and precludes a private language. We can look at a Pollock and agree on whatit “is saying” because we agree on the use of symbols. In that it is different fromthe recognition of a cup, a mountain, or any other noun. Artworks encodemeaning. And because they do, they have value. But for our current purposes it isimportant to note that the act of looking at an artwork is similar to looking at anyother object in that we have not only jointly defined that object, but because theneurological process of viewing an external, empirical object is identical in eachof us and involves well known processes.

But the same thing would not be true from the point of view of the artist. Ifthe hippocampus controls memory acquisition for cognitive functions while thebasal ganglia controls memory acquisition for habit formation and proceduralmemory, it is reasonable to postulate that creative thinking and production involveat least both of those regions of the brain, though again, that relationship maysometimes be competitive while at other times being cooperative. Memories arestored in the brain through the cellular processes involved in the changes ofsynaptic strength, and the hippocampus is the source for processing memoryabout events and ideas. While the ability to discuss others' work (i.e., to functionas a critic) would involve the articulation of facts in the world that are similar todiscussing other third-person objects, the ability to make objects would stem fromother sources especially the habit formation region. It takes a lot of repeatedactions to form a habit; it takes a lot of making art to find one’s practice – to findone’s own voice. This would make those regions of the brain much moresignificant for the artist than for the critic.


Thus the inability of an artist to discuss his or her own work would mean thatthe process of making the work is stored in memories not accessible to thedeclarative memory system, such that it would instead be stored in the variouskinds of nondeclarative memory systems including procedural, perceptuallearning, and classical conditioning. We can know but we can not know that weknow, or perhaps more exactly, we cannot access language to tell either ourselvesor others what it is that we know. And that’s ok. It is perhaps exactly the notion ofwhich van Gogh was aware: “…putting it on paper is, alas, not so easy as lookingat it.”

And it is the looking at it that counts. Viewers recreate and re-experience theartist’s viewpoint. I look at a van Gogh and experience the tumultuousness, theintense emotionality in the instability of the world as it transitions from ultra-saturated and ultra-ripe to almost ebullient decay. Aesthetic theories have oftenweakly accomplished their goal of explaining what art is doing and how it isdoing it. It is perhaps partially due to the reliance on language as philosophy is ofcourse corseted by that milieu, but it is also because aesthetics has seen art aseither a material artifact easily translatable into language; an experience similar toother physical pleasures; or a platonic transformation of beauty into fact. It is noneof those things. It is the recreation in the viewer of the various parts of the artist’sbrain that is constituent of experience qua experience: the intense “gist” recordedin the amygdala, the procedural learning in the caudate, the habit formation alongwith the perceptual learning and conditioning in the larger basal ganglia,combined again with the episodic memories stored in the hippocampus and thatregion’s role in the declarative memory system. It is experience, experienced bothexplicitly and implicitly. The experience of an individual, embedded in a physicalbody, the caretaker of memories, processer of thoughts; experience profoundlyfelt and deeply cared about. Whether they will find that in one of the 2,500 slicesof H.M.’s brain is to only be hoped…

Works Cited

Adolphs, R., Tranel, D. & Buchanan, T. W. (2005). Amygdala damage impairsemotional memory for gist but not details of complex stimuli. NatureNeuroscience, 8, 512-518.

Budson, A. E., Price, B. H. (2005). Memory dysfunction. N Engl J Med., 352, 7.Diekelmann, S. & Born, J. (2007). One memory, two ways to consolidate? Nature

Neuroscience, 10, 1085-1086.


Gabrieli, J. D. E. (1998). Cognitive neuroscience of human memory. Annu. Rev.Psychol., 49, 87-115.

Goda, Y. (2008). Along memory lane. Nature, 456, 590-591.Houk, J. C., Davis, J. L. & Beiser, D. G. (1995). Models of Information of

Processing in the Basal Ganglia. Cambridge, Mass: MIT.LeDoux, J. (1996). The Emotional Brain. NY: Simon & Schuster.Ofen, N., Kao, Y. C., Sokol-Hessner, P., Kim, H., Whitfield-Gabrieli, S. &

Gabrieli, J. (2007). Development of the declarative memory system in thehuman brain. Nature Neuroscience, 10, 1198-1205.

Packard, M. G. & Knowlton, B. J. (2002). Learning and memory functions of thebasal ganglia. Annu. Rev. Neurosci., 25, 563-93.

Poldrack, R. A. & Packard, M. G. (2003). Competition among multiple memorysystems: converging evidence from animal and human brain studies.Neuropsychologia, 1497, 1-7.

Poldrack, R. A., Prabhakaran, V., Seger, C. & Gabrieli, J. D. E. (1999). Striatalactivation during cognitive skill leanrning. Neuropsychologia, 13, 564-74.

Sherry, D. F. & Schacter, D. L. (1987). The evolution of multiple memorysystems. Psychol. Rev., 94, 439-54.

Squire, L. R. (2004). Memory systems of the brain: a brief history and currentperspective. Neurobiology of Learning and Memory, 82, 171-177.


Chapter 2

THE STRUCTURE AND FUNCTION OF VEGETALECOSYSTEMS OF SEMIARID AREAS IN

NORTHEASTERN MEXICO

P. Rahim Foroughbakhch*, Glafiro J. Alanis Flores,Jorge L. Hernández Piñero and Artemio Carrillo Parra

Facultad de Ciencias Biológicas, UANL, A.P. F-2, 66451 San Nicolás de losGarza, N.L., México

Introduction

True solutions to the problems of arid and semiarid zones throughout theworld require a mandatory previous evaluation of the natural resources from thestudy areas. Our main approach is driven to the structure and functioning of thevegetal ecosystems which intimately involves various interrelated elements suchas flora, cattle, and other fauna, taking also into account management andcommercialization of wooden and non wooden products.

A great part of the Mexican republic is located in the world desert belt withapproximately 20 million hectares, 18 million located in the northeast of thecountry, mainly covered by xerophytic shrub vegetation (Rzedowski, 1978,Palacios, 2000). There is no evidence to date about the origin of the differenttypes of shrub populations growing as natural climax vegetation.

* E-mail address: [email protected] rahimforo @hotmail.com. Tel/Fax (+52) 8181143465

P. Rahim Foroughbakhch, G.J. Alanis Flores, J.L. Hernández Piñero et al.22

Structurally, a great part of the semiarid ecosystems is composed of woodenshrubs somehow called “matorral” or thornscrub growing under diverse climaticconditions all over from stony or rocky soils, saline and non saline areas withaccelerated erosion to areas located at the base of the mountains (SAG, 1975).This type of vegetation is characterized by the occurrence of diverse structures(Follet, 2001), biological forms and floristic diversity (WWF, 2001). The practiceof under-controlled overgrazing in this area ranks in the third place in importancewithin the forest destruction causes after agricultural clearing and irrational forestexploitation. This is done because of the need of the farmer to survive who incursto cattle ranch activities but without considering a sustained use of the vegetationneither the implementation of management techniques which unavoidably affectthe vegetation at the end (French, 1979).

It is important to remark that despite anthropogenic activities have beensevere in some localities they have not yet replaced or totally eliminated theecosystems in semiarid zones (Crains et al., 2000). However, current scrubs couldbe occurring as secondary or disclimax vegetation as a consequence of theovergrazing of savanna scrubs, i.e. open scrubs mixed with natural pasture orgreat extensions of natural pasture disseminated even before colonial times.Similar circumstances have occurred in the invasion process of shrubs and woodyvegetation over great extensions of pasture and open savanna throughout thesouthwest of the United States of America (Hastings, 1965; Jacoby, 1985) whichis believed that extended up to the borders of the Tamaulipan Biotic Province(Johnston, 1963; Archer et al., 1988).

Johnston (1963) made a synopsis of the natural vegetation of southern Texasand northeastern Mexico based in documents from chronicler of the XIX century.He mentions that the dominant vegetation was the matorral or scrub, as it is stillnow, with the exception of two sandy and coastal plains, the White Horse Desertat the south of Texas and the sandy-calcite plains at the northeast of Soto de laMarina, Tamaulipas, which originally were pasture extensions of edafic climaxtype that were later invaded by scrub vegetation (Peñaloza and Reid, 1989).

Rojas Mendoza (1965) confirmed that the vegetation of the coastal plains hasthe typical scrub physiognomy with more or less abundant grass in the clear openspaces of the matorral. The climax pasture occurs in southeastern Mexico only inlimited portions of the Sierra Madre Oriental Mountains or in other certain areasof the North. Regarding to those changes caused to the original vegetation bycattle it is mentioned that certain groups of plants have remarkably decreased,such as herbs and grasses, while others have increased in density: shrubs, dwarftrees and some succulents such as “nopal” cactus, although the general

The Structure and Function of Vegetal Ecosystems… 23

physiognomy has been maintained (Maldonado, 1992; Návar, 2004,Foroughbakhch et al., 2006).

Study Area and Environmental Factors

The study area is located in northeastern Mexico between 22°13’ and 25° 10’North latitude and 97°45’ and 103°50’ West longitude. It limits to the North withthe United States of America, Durango and Chihuahua States to the West, andZacatecas and San Luis Potosi States to the South.

The Northeast of Mexico is constituted by three States with distinctphysiognomy each: a) Coahuila with a surface of 149,511 km2 (7.68%), b) NuevoLeon with 64,210 km2 (3.29%) and c) Tamaulipas with a surface of 78,932 km2(4.05% of Mexico’s surface).

According to the Köppen climatic classification this region is located underthe influence of a climate ranging from hot semidry, semiarid, subhumid, andtemperate to a very dry and desert climate with an average annual temperature of22-26°C and a mean precipitation of 600-800 mm. Thus the climate is extreme.

Precipitation intensity (amount per unit time period), soil characteristics (e.g.,texture and antecedent moisture conditions), and soil-surface features togetherdetermine whether precipitation events result in infiltration or runoff (Whitford2002, Breshears et al., 2003). If precipitation intensity exceeds the soil infiltrationrate, runoff will be generated – increasing the potential for soil erosion.

Soils in northeastern Mexico vary according to the FAO/UNESCO/ISRICclassification from vertisol, chesnut towards the south, good for agriculture, andleptosol in mountain forests, to regosol, calcisol and greyzem in the North area. Inthe Mountain chain areas litosol and xerosol with superficial hard rock occurs.Soil resources, including mineral nutrients, organic matter (including litter),water, and soil biota, are fundamental determinants of ecosystem structure andfunction (Jenny 1980, Vitousek 1994, Reynolds et al. 2003). Due to low rates ofweathering and pedogenic processes in dryland environments, the relativeimportance of parent material as a determining factor of soil properties generallyincreases with aridity (Jenny 1941).

In general, water has been described as the soil resource that most commonlylimits the productivity of dryland ecosystems (Noy-Meir 1973, Ehleringer et al.2000). Water resources in northeastern Mexico have been considered as low orscarce. It is important to remark that there are interconnections between thesurface and subterranean water bodies and that the former integrate hydrologicbasins which represent ecological systems where main streams are interconnected.


The retention of limiting water and nutrient resources is essential for sustainingthe structure and functioning of dryland ecosystems (Ludwig and Tongway 1997,2000). Dynamic soil properties important for water and nutrient retention includesoil structure, infiltration capacity, soil-surface roughness, organic matter content,soil aggregate stability, and soil biotic activity (Herrick et al., 2002).

Vegetative Types of Desert (BW), Semidesert (BS)and Temperate Subhumid (C(W)) Ecosystemsin Northeastern Mexico

At a broad level, vegetation is generally recognized as the dominantfunctional type in terrestrial ecosystems. In addition to conducting photosynthesis,aboveground structures of vascular plants protect soil from erosive raindrops,obstruct erosive wind and overland water flow, and enhance the capture andretention of soil resources (Miller & Thomas, 2004). Vegetation provides fuel forfire, as well as resources and habitat structure for belowground and abovegroundorganisms ranging from fungi and bacteria to birds and large mammals (Wardle2002). Finally, carbon storage and the mediation of earth-atmosphereenergy/water balances are additional plant functions that are increasinglyemphasized by researchers investigating global-change processes (Breshears andAllen 2002, Asner et al. 2003). Small trees, shrubs, dwarf shrubs, and perennialgrasses are the vegetative life forms with the greatest effects on the structure andfunctioning of dryland ecosystems.

Vegetation is considered as the most immediate manifestation of theenvironmental factors. Due to these factors major parts of the coastal plain iscovered by matorral. The vegetation types occurring in northeastern Mexico havebeen classified by COTECOCA (1973) and Rzedowski (1978) according to theirbotanical and ecological characteristics in the following groups:

Semiarid Ecosystems with Tall Shrubs

Structurally the following types of vegetation dominate in these ecosystems:


High Thornscrub

This type of vegetation is located at the piedmont of the Sierra MadreOriental adjacent to the subinermis high matorral and the subinerm mediummatorral; it is characterized by the predominance of tall shrubs or small trees 3 to5 meter high. The dominant species in the area are Helietta parvifolia, Diospyrospalmeri, Acacia berlandieri, Celtis pallida, Zanthoxylum fagara, Acacia rigidula,Cordia boissieri, Leucophyllum texanum, Pithecellobium pallens, and Schaefferiacuneifolia.The most abundant graminae are: Bouteloua filiformis, Leptochloadubia, Bouteloua trífida, Setaria marcostachya and Cenchrus pauciflorus.

Tamaulipan Thornscrub

This community is located in the North of Nuevo León State, extending intoCoahuila to the North and Tamaulipas to the Northeast. From the historical pointof view the description of the biotic unit of the Tamaulipan thornscrub is owed toMuller (1974) defining it as an ecological system of a great floristic diversity withtall thorny arboreal species including abundant herbaceous and grass species. Thisauthor located its distribution to the east of Sierra del Carmen and Sierra MadreOriental in Coahuila, in the southeast of Texas, in the north of Nuevo León andTamaulipas and in the coastal plain of the Gulf of Mexico, listing thecharacteristic species Acacia rigidula, Cercidium texanum, Acacia berlandieri,Lippia ligustriana, Guaiacum angustifolum, Acacia farnesiana, Castela teaxana,Prosopis glandulosa, Colubrina texensis, Cordia boissieri, Shaefferia cuneifolia,Lantana camara, Parkinsonia aculeata, Diospyros texana, Lycium berlandieri,Lysium pallidum, Viguiera stenoloba, Bumelia lanuginosa, Yucca rostrata,Opuntia lindheimeri, Eysenhardtia texana, Sophora secundiflora, Leucophyllumminus, Salvia balloteaflora, Celtis pallida, Opuntia leptocaulis, Condalialyciodes, Jatropha dioca, Koeberlinia spinosa, Opuntia imbricada, Agavelechuguilla, Condalia hookeri, Leucophyllum frutescens, Bernardia myricaefolia,Forestiera angustifolia, Citharexylum berlandieri, Karwinskia humboldtiana andMicrorhammus ericoides. He also reports that the genera Larrea and Flourensiaare also present in the North part in the borders of Nuevo León and Coahuila.

It is important to notice that in the Tamaulipan thornscrub is possible to findsmall patches of Quercus (Quercus virginiana, Q. fusiformis) in alluvial soils andhumid habitats at the mid north of the State living together with some otherspecies such as Sapindus saponaria.


Caducifolious Prosopis Forest

This vegetal community broadly distributed in Mexico, sometimes asapparent secondary vegetation. It is frequently found growing within thecommunities of the Tamaulipan thornscrub in deep clay soils over river terraces orstream banks where mezquitales are located with both high capacity to adapt toclay soils and high salinity tolerance. This type of vegetation is dominated bymesquite species with arboreal components 4 to 10 meter high with well definedtrunks wider than 60 cm in diameter. This type of vegetation is surrounded by thehigh thorny matorral which is characterized by the predominance of leguminosaespecies. The most important representatives are Prosopis glandulosa, Acaciaberlandieri, Acacia wrigthii, Acacia rigidula, Celtis pallida; Ebenopsis ebano,Opuntia engelmannii, Randia rhagocarpa, Yucca filifera, and Zanthoxylumfagara. The most abundant graminae are Bouteloua trífida, Aristida spp. andSetaria macrostachya.

It is common in northeastern Mexico to find mixtures of mesquite withOlneya tesota and Cercidium spp being the latter the dominant species. Due totechnical and methodological characteristics these two species and part of thethorny forest or lower thorny forest are joined in this group.

Submountain Matorral

This community is mainly composed of inermis (without prickles or thorns)and caducifolious elements of short duration. It grows between the borders of thearid matorral, oak forest and lower caducifolious forest, especially both lowersides of the Sierra Madre Oriental in their northern portion.

It is a shrubby and arboreal formation rich in a variety of life forms. Vigor,size and distribution of the dominant and co-dominant species are subjected to thetype of soil and its available moisture content. The occurring dominant biologicalforms are shrubs or trees 4 to 6 meter high with short leaves, caducifolious andsubthorny. It is located in the lower slopes of the Sierra Madre Oriental mountainchain. In fact this type of vegetation forms an extended barrier that separates theelements of the tamaulipan arid and thorny matorral in the plains from thesubhumid or dry forest occurring in the mountain slopes.

Despite the existence of some morphological and ecological variations themost representative species in general are: Helietta parvifolia, Cordia boissieri,Gochnatia hypoleuca, Neopringlea integrifolia, Decatropis bicolor, Fraxinusgreggii, Havardia pallens, Leucophylum frutescens, Acacia amentacea, Acacia


berlandieri, Acacia farnesiana, Caesalpinia mexicana, Prosopis spp Dyospirospalmeri, D. texana, Zanthoxylum fagara, Flourencia laurifolia, Karwinskiahumboldtiana, Leucophyllum sp and Pithecellobium pallens. In some protectedhabitats with plenty of moisture and deep soil some small clusters of Quercus canbe found.

In this type of vegetation the Chinese palm presence is remarkable due to itsaspect. Other species occurring in this matorral are Solanum erianthum,Mascagnia macroptera, Jacobinia spicigera, Hibiscus cardiophyllus and Rubusafftriviales.

Herbaceous Vegetation – Grazing Lands

Grazing lands are vegetal communities characterized by the dominance ofherbaceous species with long and narrow leaves specially graminae althoughsome other families may also be present such as Compositae, Leguminosae andChenopodiaceae. The natural climax grazing lands occupy reduced extensions andcommon associations of graminoids are found in open spaces within some othercommunity types such as the matorral. In special sites with specific edaphicconditions, soil with insufficient drainage, prone to floods and with abundant saltsand gypsum some grazing species, called gypsophiles, may also be found.

In the northeast of Mexico there is a partial open grazing land which ischaracterized by the presence of such species as Bouteloua gracilis, Boutelovacurtipendula, Boutelova irsuta and Tridens muticus. In gypsum calcareous soilBouteloua chasei is also found. This gypsophilous grazing land can be foundmixed with the desert microphyllous scrubland.

In saline soils and closed basins it is found an open halophyte graze land withthe following typical species: Hilaria mutica, Hilaria jamesii, Sporobolus tiroidesand S. pyramidatus. Besides, there may be mixtures of halophyte shrubby scrubspecies such as Atriplex canescens, A. confertifolia and succulent species such asSauceda spp. A variant of this type of graze land is located in small sites in openareas of the thorny tamaulipan like scrubland.

The open bunch grazing land located in shallow soils of colluvial originoccurs in the transition zone between the halophyte grazing land and themountainous zone. This graminae community is characterized by the presenceof plants with thin, narrow and long leaves; botanically these leaves arefasciculate which confer a bunchy aspect to the grazing land. The speciesbelonging to this community are Bouteloua gracilis, Boutelova curtipendula, B.


uniflora, Tridens muticus, Eragrostis hagens, Heteropogon contortus andMuhlenbergia mundula.

Semiarid Ecosystems with Median Size Shrubs

Intermediate Subinerm Matorral

It is found adjacent to the high subinerm matorral and the high thornymatorral and it is characterized by the predominance of middle size shrubs 1 to 2meters high. The most important components are Acacia rigidula, Cercidiumfloridium, Celtis pallida, Prosopis glandulosa, Condalia obovata, Acaciafarnesiana; while the most important graminae species are: Bouteloua trifida,Cenchrus pauciflorus and Leptoloma cognatum.

Subtropical and Temperate Ecosystems with Mediumand Tall Trees

Sclerophyll Oak Forest

It is located in the mountainsides; east to the Sierra Madre Oriental chainand adjacent to the pine-oak forest and to the high subinerm matorral. It ischaracterized by the presence of mid size trees 8 to15 meters high. It is aforest inhabited by individuals of the genus Quercus in highly diverseecological conditions at altitudes from 600 to nearby 2000 masl. The mainspecies in this type of vegetation are the following: Quercus fusiformis, Q.laceyi, Quercus canbyi, Quercus glaucoides, Juglans spp., Hicoria pecan,Ungnadia speciosa, Arbutus arizonica. The most abundant graminae are:Setaria texana, Bouteloua curtipendula, B. hirsuta, and diverse species ofBromus spp.

Pine And Oak Forest

It is located in the upper parts of the Sierra Madre Oriental Mountainsadjacent to the oak forest. It is composed of trees 10 to 18 meters high dominatedby the species of the genus Pinus and Quercus. It develops under different


ecological conditions, being frequent in forest areas highly exploited or indisturbed conditions of the pine or pine-oak forest.

The dominating species in this vegetation type are the following: Quercusrysophylla, Quercus polymorpha, Quercus canbyi, Quercus mexicana, Quercuscupreata and different species of pines such as Pinus pseudostrobus, P.montezumae, P. teocote, Arbutus xalapensis and Ugnadia speciosa. The mostimportant graminae species are: Bouteloua curtipendula, Setaria texana, and Poamulleri.

Lower Caducifolious Jungle

It is a jungle which may reach 10 to 15 m (in tropical and subtropicalecosystems of Tamaulipas State) growing in warm subhumid, semidry or dryclimate where most of the individuals (75-100%) throw their leaves during the dryseason which is long lasting. Dominant trees are commonly inermis. It is broadlydistributed over the sides of hills with well drained soils in the coast of theTamaulipas State and may be in contact with the intermediate jungle, forest andmatorral of semiarid zones. It is frequent to find communities of Bursera spp,Lysilomia spp, Jacaratia mexicana, Ipomoea spp, Pseudoborbax palmeri,Erithryna sp., Ceiba sp. and Cordia sp.

Arid Ecosystems with Short Shrubs

Desert Vegetation

This community is the most representative of the arid zones of northeasternMexico where numerous endemic species grow. These species are identified inthe official list of the Mexican government for species at risk NOM-059-SEMARNAT-2001 in any of the existing risk categories (Table 1). This type ofmatorral is described as follows.

Table 1. List of the occurring flora in the matorral and grazing lands of NuevoLeón State as for the norm NOM-059-SEMARNAT-2001.

Family Genus Species Common name in the studied area (Spanish) Category DistributionAgave Bracteosa Maguey huasteco T Not endemicAgavaceaeAgave Victoria-reginae E EndemicAriocarpus Kotschoubeyanus Biznaga-maguey pata de venado Pr Not endemicAriocarpus Retusus Biznaga-maguey, peyote común Pr Not endemicAriocarpus Scapharostrus E EndemicAriocarpus Trigonus Biznaga-maguey chautle T EndemicAstrophytum Asterias Biznaga-algodoncillo de estrella, cacto estrella E EndemicAstrophytum Capricorne Biznaga-algodoncillo de estropajo T EndemicAstrophytum Myriostigma Biznaga-algodoncillo de mitra T EndemicAstrophytum Ornatum Biznaga-algodoncillo liendrilla T EndemicAztekium Hintonii Biznaga-piedra del yeso Pr EndemicAztekium Ritteri Biznaga-piedra viva T EndemicCoryphantha Poselgeriana Biznaga-partida de Poselger T EndemicCoryphantha Pseudoechinus Biznaga-partida de falsas espinas Pr EndemicEchinocactus Platyacanthus Biznaga-tonel grande Pr Not endemicEchinocereus Knippelianus Órgano-pequeño T EndemicEchinocereus Longisetus Órgano-pequeño de cerdas largas Pr EndemicEchinocereus Poselgeri Sacasil Pr Not endemicEchinomastus Mariposensis T EndemicEpithelantha Micromeris Biznaga-blanca chilona Pr Not endemicFerocactus Pilosus Biznaga-barril de lima Pr Not endemicGeohintonia Mexicana Biznaga-del yeso Pr EndemicLeuchtembergia Principis Biznaga-palmilla de San Pedro T EndemicMammillaria Carretii Biznaga de Icamole Pr EndemicMammillaria Klissingiana Biznaga de Calabazas T EndemicMammillaria Lenta Biznaga de Biseca T EndemicMammillaria Plumosa Biznaga plumosa T Endemic

Cactaceae

Mammillaria Sanchez-mejoradae E Endemic

Table 1. Continued

Family Genus Species Common name in the studied area (Spanish) Category DistributionPelecyphora Strobiliformis Cacto piña de pino T Not endemicPeniocereus Greggii --- Pr Not endemicThelocactus Macdowellii Biznaga-pezón de Macdowell T EndemicThelocactus Tulensis Biznaga-pezón de Tula T EndemicTurbinicarpus Beguinii --- Pr Not endemicTurbinicarpus Hoferi Biznaga-cono invertido de Hofer T EndemicTurbinicarpus Mandrágora Mandrágora T EndemicTurbinicarpus Pseudopectinatus Peyotillo pectinado Pr Not endemicTurbinicarpus Schmiedickeanus Uñita T EndemicTurbinicarpus Subterraneus Biznaga-cono invertido subterránea T EndemicTurbinicarpus Swobodae Biznaga-cono invertido T EndemicTurbinicarpus Valdezianus Biznaga-cono invertido de Valdez Pr Not endemic

Frankeniaceae Frankenia Johnstonii --- E Not endemicCalibanus Hookeri ---NolinaceaeDasylirion Longissimum Sotol vara cohete, junquillo, sotol manso T Not endemic

Palmae Brahea Berlandieri Palma Berlandier Pr EndemicZamiaceae Dioon Edule Chamal o palma de Dolores T Endemic

T= threatened, Pr= protected, E= endangered


Desert Rosetophyllous Vegetation

This is a vegetal community characterized by the dominance of species withrosette like leaves with or without thorns, generally acaulescent (stemless)although rosette species with well developed trunks are typical. This communitygrows in the slope of the hills over rock outcrops or skeletal soils or lithosols,over shallow soils in lower hills, in the higher parts of alluvial fans or overconglomerates. Its distribution is wide over arid and semiarid zones. In these areasthe matorral lose vigor and covering, decreasing its floristic richness and the sizeof the individuals of the main species. The components that dominate thismatorral are conspicuous elements characterized by presenting succulent leavesgrouped in rosettes, some with terminal thorns each called a mucron.

The most common species are Dasylirion berlandieri y D. texanum, Hechtiaglomerata, Agave striata, Agave bracteosa, Agave lechuguilla, Euphorbiaantisyphilitica, Opuntia leptocaulis, Opuntia microdasys, Echinocactusplatyacanthus, Ferocactus pilosus and Oputia spp. In some places Yuccatreculeana, Dasylirion longissimum and Fouqueria splendens are found.

Desert Microphyllous Vegetation

This type of vegetation is characterized by the dominance of shrub specieswith short aromatic leaves or leaflets growing mainly over alluvial terrains in aridand semiarid zones in the north of Mexico. Numerous cacti of round or plainstems are found and other plants such as the desert plant (Yucca filifera) as themost noticeable elements. It is located abundantly in plain terrains or in loweralluvial fans of the Mexican Altiplano hills.

The typical species of this type of vegetation are: gobernadora (Larreatridentata), hojasén (Flourensia cernua), Mariola (Parthenium incanum),candelilla, albarda, afinador (Mortonia greggii), guayule (Partheniumargentatum), quebradora (Lippia ligustrina), popotillo (Ephedra aspera), biznagade dulce, chaparro prieto, chaparro amargoso, huajillo, granjeno, mezquite, palmapita, palma samandoca (Yucca carnerosana), órgano (Marginatocereusmarginatus), nopal cegador (Opuntia microdasys), coyonostle, nopal rastrero(Opuntia rastrera) and tasajillo. In some places in the south of the State succulent,rosette scrubs or crassulaceae are found i.e. branched cacti with succulent stemswhere garambullo (Myrtillocactus geometrizana) is the most noticeable species.


Desert Microphyllous Vegetation with Palms or Izote Plants

This is a variant of the desert microphyllous vegetation with abundantcompact groups of desert palms or pita palms with remarkable elements oreminences making some small forest patches of palms associated to this type ofscrub. They are located in flat places or alluvial fans of the hills on the MexicanAltiplano. The main species that make this type of vegetation are Palma loca(Yucca filifera) and Palma samandoca. Typical species are: gobernadora, hojasén,panalero, cruceto (Condalia lycioides), chaparro prieto, agrito (Berberistrifoliata), popotillo (Ephedra aspera), tasajillo, coyonostle, nopal rastrero andnopal cegador.

Sand Desert Vegetation

These are vegetation patches invading dunes or arid zones getting establishedprogressively, generally coming from neighboring areas which are frequentlycomposed of Prosopis spp (mesquite tress), Larrea tridentata (gobernadora),Opuntia spp (nopal), Atriplex spp (saladillo), Ambrosia dumosa (hierba del burro),Ephedra trifurca, Dalea emoryi, Eriogonum desertícola, Petalonyx thurberi,Coldenia palmeri, Hilaria rigida, Hymenoclea monogyra, etc.

The presence of the above mentioned species in the different ecosystemsindicates that they are capable to adapt to the different ecological conditionsshowing their high competitive advantage in so extremely different ecosystems.

Biological and Utility Value of Plants from Aridand Semiarid Ecosystems

With the established methodology all the taken data allowed to make astructural analysis on the arid and semiarid ecosystems in northeastern Mexicobased in the existing vegetal resources, their use and management of each one ofthem. This analysis has contributed with useful information about the floristiccomposition of vegetal ecosystems, the number of species and their ecologicalbehavior.

Vegetal communities in interrelation with the climatic, topographic, and soilelements favor the survival and habitat of the wild and domestic fauna in additionto the basic and sub-basic products they provide to mankind. Tropical andtemperate forests as biological units of dense covering offer ecological stability in


their function as carbon capture, hydrologic cycle, soil protection andbiodiversity. In a comparative way the arid and semiarid zones, where vegetalcommunities with scrub and grazing lands of low covering are present representthe typical characteristics of the ecological conditions of these areas hencerational management of the existing resources might be applied without anytransgression to the rules that govern their functioning since these are fragileecosystems of difficult recovering.

Typical scrub and grazing lands of northeastern Mexico are the masterpiececonsequence of evolution and time with a legacy that has resulted in an importantecological biodiversity for hundreds of years which is today admired and used.This plant community extends over plains, hills, crests, slopes and canyons. Theplant species composing scrubs and grazing lands and their associations make avegetal coverage poorly dense although providing a series of products andmultiple services from the ecological perspective.

The great floristic diversity existing in the different arid and semiaridecosystems in northeastern Mexico offers the availability of a comprehensive“material bank” useful to provide:

Table 2. Some species of the useful flora of arid and semiarid ecosystems ofnortheastern Mexico.

Usage Common name inthe area(Spanish) Scientific name Part used and/or

methodMesquite Prosopis glandulosa Full stem or sawn

lumberEbano Ebenopsis ebano Full stemEncino molino, E.bravo o E.siempreverde

Quercus fusiformisQuercus virginiana

Full stem or sawnlumber

Palma pita Yucca filifera Stems in house wallsand fences

Palma samandoca Yucca carnerosana Stems in house wallsand fences

Construction

Sotol Dasylirion texanum Leaves for roof andsheds

Mezquite Prosopis glandulosa Stems for polesEbano Pithecellobium ebano Stems for polesBarreta Helietta parvifolia Stems for shelvesAnacahuita Cordia boissieri Stems for shelvesHuizache Acacia farnesiana Stems for shelves

Shelves andcattle fences

Brasil Condalia hookeri Stems for shelves


Table 2. Continued


methodAlbarda Fouqueria splenden The whole plantNopales Opuntia ssp The whole plantPlama pita o china Yucca filifera The whole plant

Living fences

Palma samandoca Yucca carnerosana The whole plantTenaza Havardia pallens Stem for shovel

shaftsBarreta Helietta parvifolia Stem for axe handlesAnacachuita Cordia boissieri Stems for plowsGuayacán Guaiacum

angustifoliumStem for machetehandles

Making of tillageinstruments

Coma Burnelia lanuginosa Stems for yokebeams

Tenaza Havardia pallens Stem for chairs androckers

Tenaza Havardia pallens Stems for basketsand moorings

Mezquite Prosopis glandulosa Stems for chairs andbanks

Furniture andutensilsmanufacture

Ébano Pithecellobium ebano Stems for chairs andbanks

Mezquite Prosopis glandulosa For wagon axlesConstruction ofrural transportationdevices Ébano Ebenopsis ebano Hard stems for tables

and wagon wheelsMezquite Prosopis glandulosa Stems and branches

Ébano Ebenopsis ebano Stems and branches

Firewood andvegetal charcoal

Brasil Condalia hookeri Stems and branches

Lechuguilla Agave lecheguilla Ixtle fibers

Cortadillo Nolina sp Fibers from theleaves

Palma pita Yucca filifera Palm ixtje fibersfrom the leaves

Fiber production

Palma samandoca Yucca carnerosana Palm ixtje fibersfrom the leaves

Candelilla Euphorbiaantisyphilitica

Wax from the stemsProduction of waxand raw materialfor rubbermanufacture

Guayule Partheniumargentatum

Raw materials fromthe whole plant


Table 2. Continued


methodMezquite Prosopis glandulosa The whole plantCenizo Leucophyllum

texanumThe whole plant

Ébano Ebenopsis ebano The whole plantHierba del potro Caesalpinia mexicana The whole plantAnacahuita Cordia boissier The whole plantLaurel de montaña,colorín

Sophora secundiflora The whole plant

San Pedro otronadora

Tecota stans The whole plant

Lantana Lantana cámara The whole plantPalma pita Yucca filifera The whole plant

Native flora usedas ornamentals.

Encino molino,encinobravo

Quercus fusiformisQuercus virginiana

The whole plant

Orégano de la sierra Monarda citriodora Leaves forcondiments

Condiments

Oreganillo Lippia graveolens Leaves forcondiments

Calabacilla loca Cucurbita foetidissima Raw fruitAmole de castilla Agave bracteosa Stems and roots

Soap anddetergentsubstitute Jaboncillo Sapindus saponaria Ripe fruit

Nopales Opuntia ssp Ripe fruit, youngstem

Chile de monte ochile piquín

Capsicum annum var.aviculare

Fresh and dry fruits

Pitaya Selenicereusspinulosos

Ripe fruits

Maguey Agave americana Cooked flower stem(quiote)

Palma pita Yucca filifera Flowers for preparedfoods and salads

Palma samandoca Yucca carnerosana Flowers for preparedfoods and salads

Ébanos Ebenopsis ebano Seeds as coffeesubstitute

Granjeno Celtis pallida The ripe fruit isconsumed

Brasil Condalia hookeri The ripe fruit isconsumed

Coma Bumelia lanuginosa The ripe fruit isconsumed

Food

Mezquites Prosopis glandulosa Fresh and dry fruits


Table 2. Continued


methodMaguey Agave americana Beverages and “mezcal”

from stemsAlcoholicbeverages

Sotol Dasylirion texanum A “sotol” called beverageobtained from stems

Mezquite Prosopis glandulosa Leaves, young branchesand fruits

Cuajillo Acacia berlandieri Leaves and youngbranches

Vara dulce Eysenhardtiapolystachya

Leaves and youngbranches

Guayacán Porliera angustifolia LeavesNopales Opuntia ssp Young and mature stemsChamizo Atrilpex canescens Leaves and young stemsNavajita roja Bouteloua trifida Leaves and young stemsNavajita azul Bouteloua gracilis, Leaves and young stemsZacate toboso Hilaria mutica Leaves and young stemsPajita tempranera Setaria spp. Leaves and young stems

Forage

Zacate piramidal Sporobuluspyramidatus

Leaves and young stems

a). Food, fuel, fiber, forage, raw material for industries (wax, tannins,pigments) and constructions materials (Table 2).

b). Medicines, taking into account that Mexico counts with about 3,000species recognized by their curative properties, many of them growing inarid and semiarid ecosystems of northeastern Mexico (Table 3).

c). Toxic plants with their toxic compounds useful in many cases for healingtumor diseases (Table 4).

Overview of the Sustainability and Conservationof Dryland Ecosystems

According to Miller & Thomas (2004) sustainable ecosystems, as defined byChapin et al. (1996), are persistent. Through the typical pattern of dynamicsdriven by disturbance events and climatic fluctuations such ecosystems maintaintheir characteristic diversity of their major functional groups, productivity, andrates of biogeochemical cycling. Inherent in the notions of ecosystemsustainability and persistence is the hypothesis that ecosystems can be driven totransition from one state to an alternative state. Though capable of existing in the


same physical location, these alternative ecosystem states are distinguished byrelatively large differences in their structure and their rates of ecological processessuch as erosion, nutrient cycling and disturbance. Such differences in structureand processes typically are matched by great differences in ecosystem dynamics.A transition from one state to an alternative state may occur gradually orrelatively rapidly as the result of natural processes (e.g., climatic disturbances) orhuman actions (e.g., land-use activities).

Table 3. Medicinal species, their abundance-dominance and usesof the flora in Villa Santiago, Nuevo León, Mexico.

(Source: Valdez Tamez, 2002).

Scientific name Common name inthe area (Spanish) A.L. Illness or

diseasePart of theplant used

Way ofuse

Acacia amentáceaD.C.

Chaparro Prieto 3 Antibiotic Bark H (O)

Acalyphahederácea Torr.

H. del cáncer 1 Wounds, acne Plant H (L)

Achilleamillefolium L.

Plumajillo Insomnia,headaches

Leaves C,I (O)

Ageratumcorymbosum Zucc.

H. dulce N.A. Leaves I (O)

Antigonon leptopusHook & Am.

San Diego 1 Swelling,mumps

Leaves I (O)

Arbutus xalapensisH.B.K.

Medroño 2 Kidney ache Leaves H (O)

Arctostaphylospungens Kunth

Pinguica 3 Kidney ache Leaves/flowers

H (O)

Asclepias linariaCav

Plumita 1 Earache Leaves M (L)

Berberis gracilisBenth.

Cuasia 1 Colic, appetitesupressant

Stems H (O)

Bidens pilosa L. Acahual 1 Kidney ache Branches I (O)Bouvardiatermifolia Cav.

Candellio 1 Stitches,scratches

Leaves I (O)

Budlejascordioides Kunth

Glondrina 1 Bloodenhancer

Roots C (O)

Bumelialanuginosa Michx

Coma 2 Indigestion Bark I (O)

Castela texana Chaparro amargo 2 Diabetes Leaves andsprouts

I

Celtis pallida Torr. Granjeno 2 Headache Leaves M (L)Chilopsis linearisCav. (Sweet)

Lantrisco Expectorant Leaves,flowers

C (L)


Table 3. Continued



Way ofuse

Chimaphilamaculata L.

Jikjoñio Heartbeat Plant C (O)

Chimaphilaumbellata L.

Ontejol 1 Stomachache Leaves/stems I (O)

Chrysactiniamexicana Gray.

Talatencho 1 Stomachache Plant I (O)

Clematisdrumondii Torr. &Gray

Barba chivo 1 Acne,constipation

Leaves M (L)

Cordia boissieriDC.

Anacahuita 2 Cough, flu Fruits S (O)

Corwania plicataDC.

Rosa castilla 3 Diarrhea,stomachache

Leaves H (O)

Croton torreyanus Salvia 1 Cholesterolcontrol

Leaves H(I)

Cuphea cyaneaWald.

N.A. 1 Love potion Branches G (L)

Decatropis bicolorZucc. Radlk.

N.A. 2 Antibacterialactivity

Leaves,branches

I (O)

Desmodium psilo-phyllum Schtdl.

Pega pega 1 Dysentery Roots Lo (O)

Dondonea viscosaJacq.

Cajuele Postpartumbath

Fresh leaves I (E)

Dyssodia setifolia(Lag.)Rob.

Paraveña 1 Digestive,emotional

Leaves/stems C (O)

Equisetum leave -gatum Bartelett

Tull Diuretic,kidney ache

Branches I (O)

Eryngium serratumCav.

H. de sapo Aphrodisiac Leaves androots

I (O)

Eysenhardtiatexana scheel

Palo dulce 2 Antibiotic,urinary

Roots, stems H (O)

Flourensia cernua Leavesén 2 Diarrhea Leaves,flowers

H(I)

Geraniumseemanni Peyr.

Ranxh 3 Wounds Plant P (L)

Gymnospermanítida Rothr.

Sempiterna Heart Flowers C(O)

Gymnospermaglutinosum Spreg.

Talalencho 1 Rheumatism Leaves,branches

I (L)

Hedeomadrummondii

Poleo 2 Asthma,bronchitis

Leaves, fruits I(H M)

Heimia salicifilia(H.B.K.) Link.

Jarila 1 Stomachache,ankle ache

Leaves,branches

T (L/O)


Table 3. Continued



Way ofuse

Helietta parvifolia(Gray.) Benth.

Barreta 3 Teeth strength Stem andleaves

I (O)

Juniperusdeppeana Steud.

Sabino Chronic fatigue Stem andleaves

I (O)

Karwinskiahumboldtiana Zucc

Coyotillo 2 Antitumor Leaf andfruits

I (O)

Lantana cámara L. Crozuz 2 Cough, scare,kidney, acne

Leaves, stem H,C (O)

Lepidiumvirginicum L.

Letejilla 2 Wounds Leaves, stem H (L)

Litsea novoleontisBartlett.

Laurel 1 Cough, asthma,heart

Leaf I (L9)

Litsea prigleiBartlett.

Laurel 1 Dizziness,asthma, nerves

Leaf I (O)

Partheniumhysterophorus L.

Cunario 2 Fever, bodypain, malaria

Leaf,branches

M (L)

Partheniumincanum

Mariola 1 Arthritis Leaves,flowers

I

Pinus montezumaeLamb.

Ocote 2 Hoarseness Stem H (O)

Pinus pseudo--strobus Lindl.

Ocote 1 Hoarseness Stem H (O)

Plantgo major L. Lantén 1 Tonsils, diarrha, Leaf I,C (O)Polypodiumguttatum Maxon.

Calaguale Pain in bonds branches I (O)

Polypodiumpolypoides L. watt

siempreviva 1 Asthma branches I (O) I

Populustremuloides Michx

Alamillo Fever, pains Leaf I (O)

Pteridiumaquilinum L.

Pesma 1 Stomachache Roots H (O)

Quercus affinisScheld.

Encino bco. 3 desinflamatory Bark C (O,L)

Quercus laetaLiebm.

Encino laurel 3 desinflamatory Bark I (L)

Rhustoxicodendron L.

Hiedra 1 Skin, leprosy,scabies

Leaf, stem C (L)

Rhus virens Gray.Lindh.

Lantrisco 2 Medicinal N.A. N.A.

Stevia saliciformaCav.

Chacal 1 Tonsils,diarrhea, asthma

branches M (L)

Tagetes lucidaCav.

Pericón 1 Postpartum bath Plant C (L)


Table 3. Continued



Way ofuse

Teucrium cúbenseJacq.

Gallina ciega 1 Indigestion Leaf, roots H (E)

Salvia spp Salvia 2 Parkinson,hemorrhage

Leaf I (L)

Selaginellalepidophylla Gray

Flor de peña Kidney,bronchitis

Plant I (L)

C= cooking, E= external, H = boiled, I= Infusion, s = syrup, L= local, Lo= Lotion, M=macerated., Mo= ground, P= powder, N.A.= not available. A.D.= adaptation level: 1forlow, 2 for medium, 3 for high.

Recent studies on the earth’s climate change show an increase in temperatureconcomitant with a decrease in humidity believed to be caused by theconcentration raise of atmospheric CO2 from 265 to 350 ppm in the last 25 years.This increase represents a physiological advantage for C3 plants, mainly woodyspecies which are more adapted to these conditions than C4 plants, includinggrasses (Mayeux et al., 1991). This process supports the hypothesis that the raisein woody shrub species in the natural scrub and graze land communities of thesouth of the United States and northeastern of Mexico is related to theseatmospheric climate changes and not only attributed to human activities in termsof management (Archer 1994).

Frequently, human activities and natural processes interact each other tocause persistent transitions among states (Westoby et al. 1989, Bestelmeyer et al.2003, 2004, Stringham et al. 2003). Of greatest concern from a conservationperspective are alternative ecosystem states that reflect a degraded capacity toperform desired ecosystem functions. Ecosystems that have been driven across thethresholds of degradation cannot be restored to previous conditions simply byremoving the stressor.

In the extensive areas of the arid and semiarid ecosystems of northeasternMexico the primary use of the vegetation is based on extensive grazing. However,if livestock activities using herbaceous graminoid species were considered, theincrease in thorny woody shrub plants density would limit this activity due to alower density of graminae and other forage species and to the difficulttransportation of cattle. This situation makes it imperative the creation of strategicprograms for integral management that yield a more efficient use of the scrubphytoresources.

Table 4. Main toxic plants from northeastern Mexico.

Scientific name Common name inthe area (Spanish) Family Toxicity

Acaciaberlandieri

Huajillo Leguminosae Leaves and fruits. The N-methyl-beta-phenylethylamine is responsible forthe ataxia and prostration, sometimes death in cattle. Mechanism similar toephedrine.

Acacia gregii Uña de gato Leguminosae Leaves. Mydriasis, tachypnea, convulsions and death may occur Inintoxicated animals. Symptoms are similar to those present in hydrocyanicacid poisoning.

Agavelechuguilla

Lechuguilla Agavacea Leaves. A saponin (Hepato-nephro-toxine) and a skin photosensibilizator.

Alliumcanadense

Cebollín Liliaceae The whole plant. An undetermined alkaloid causes anemia, hematuria,gastro-enteritis and death.

Anagallisarvensis

Coralillo Primulaceae The whole plant. Toxic compounds such as glycosides triterpenes,antifungal, flavonoids and cucurbitacins. Lethal toxic effects on sheep andsymptoms such as diarrhea, anorexia and depression, injuries includingbleeding in the kidney and heart, congestion of lungs and liver damage.

Argemonemexicana

Cardo Santo Papaveraceae All the plant. Alkaloids berberine. Lung congestion and damage to kidneysin laboratory rabbits and protopine (narcotic action). These alkaloids areremoved by animals through the milk. The seed contains isoquinolinealkaloids: sanguinarine and dihydrosanguinarine which show irritant effecton skin and mucosal areas.

Asclepias latifolia

Hierba lechosa Asclepiadaceae Leaves and young stems. The galitoxin is the dominant toxicant in thelatex. In lesser quantity cardiac glycosides and poisonous alkaloids.Animals show motor and respiratory problems resulting in death.

Astragaluswootoni

Hierba loca Leguminosae The whole plant. Presence of hydrogen cyanide and other toxic substances.Alkaloids and saponins contained act as emulsifying agents for fats andhaemolysis of red blood cells due to its affinity with colestrin and lecithin.

Brassiica nigra Mostaza Cruciferae Seed. Glycoside: sinigrin. Irritating to skin and mucous membranes.Abortion.

Table 4. Continued


Centauriumcalycosum

Centauro Gentianaceae The whole plant. Concentrated bitter glycosides (gentiopicrin) duringflowering. In large amounts causes abundant watery diarrhea, polyurea,general weakness and sometimes coma and death.

Cephalanthusoccidentalis

Mimbre Rubiaceae The whole plant. Poorly studied. Cephalotin, a substance that destroys redblood cells, causes vomiting, convulsions and paralysis of the limbs.

Citrusaurantium

Naranjo Agrio Rutaceae Fruits and flowers. Three glycosides contained: Hesperidin, Isohesperidinand aurantiamarin which are sedative of the central nervous system.

Clematis dioica Barbas de chivato Ranunculaceae Leaves. Protoanemonin, Hederagenin saponins, oleanolic acid derivatives,anemonol. Used in small doses against varicose ulcers and rheumaticpains.

Convolvulusarvensis

Correhuela Convolvulaceae Tender leaves and shoots. Poisonous to livestock. There is a lack ofreliable information.

Croton ciliato Canelillo Euphorbiaceae Ripe fruits and nuts. Glycosides and resins. Just half to one drop causesburns to the digestive system.

Cuscuta sp. Tripa de Judas Convolvulaceae The whole plant. Cuscutina glucoside, resin, tannins, uncharacterizedflavonoids (stems and flowers).

Chenopodiumalbum

Hedionda Quenopodiaceae The whole plant. Cyanogenic glycosides. Ascaridol (high concentrationscause severe liver and kidney lacerations).

Chenopodiumambrosioides

Epazote Quenopodiaceae The whole plant. Ascaridol.

Drymariaarenarioides

Alfombrilla Cariofilaceae Leaves and Stems. Saponins, oxalic acid, alkaloids (thebaine, narcotine,narcein solanine and ergotoxin).

Euphorbiamaculata

Hierba de laGolondrina

Euphorbiaceae The whole plant. Rubber-resin (Euforbona).

Fluorensciacernua

Hoja Sen Compositae Dry fruits. Glycosides and a not characterized resin.

Table 4. Continued


Gutierreziasarothrae

Hierba de SanNicolás

Compositae Leaves. Saponins

Hordeumvulgare

Cebada Graminaceae The whole plant. Barley embryos develope an alkaloid (hordenine).

Karwinskiahumboldtiana

CoyotiloTullidora

Rhamnaceae Leaves. Antracen. Hindquarter paralysis and death with seeds and fruits.Weak decay with nausea.

Ligostrumjaponicum

Trueno Oleaceae Leaves and fruits. Glucoside (privet), tannins.

Lobeliaberlandieri

Diente de víbora Lobeliaceae The whole plant. Alkaloid: lobelia of sympathetic-mimetic action,antispasmodic and dilator of the bronchial tissue. Intoxication causesdecreased respiratory rate, muscle atrophy, prolonged coma and death.

Lophophorawilliamsii

Peyote Cactaceae The whole plant. Strychnine alkaloids that increase reflex irritability to thepoint of spasms. Morphine-type alkaloids have sedative and sleepingaction.

Medicago sativa Alfalfa Leguminosae The whole plant. Large ingestion causes trifoliosis with mouthinflammation and skin irritation.

Meliaazedarach

Canelo Meliaceae The whole plant. Azedarine alkaloid (central nervous system) and paraisin(insect repellent), Hemolyzing saponins and tannins, essential oils andresins.

Melilotis officinalis

Trébol oloroso Leguminosae The whole plant. Warfarin or dicumarol, anticoagulant. Coumaric andmelitotic acid.

Melochiapiramidata

Chichibe Esterculiaceae Alkaloids. Chloroform extract in mice resulted in hind incoordination,prostration and death.

Neriumoleander

Laurel Apocinaceae Glycoside. Cardiac arrhythmia, diarrhea, death.

Table 4. Continued


Nicotianaglauca

Tabaco Solanaceae Alkaloids: narcotine, colchicine, lobeline, nicotine, anabasine, and so on.The anabasine are presented in greater proportion and apparently isresponsible for poisoning.

NicotinaTabacum

Tabaco Solanaceae Leaves. Nicotine.

Nolina texana Sacahuiste Liliaceae Buds, flowers and fruits. Filoeritrina and a hepato-nephro-toxin, toxicitysimilar to that of A. lettuce.

Phoradendrontometosum

Injerto del Mesquite Lorantaceae Berries. Highly toxic phenols.

Prunus serotina Capuli Rosaceae The whole plant. Cyanogenic glycoside (amygdalin). Breathing difficulty,spasms, coma and sudden death.

Quercus spp. Encino Fagaceae Leaves and acorns. Tannic acid and other undetermined toxic precursors.Rhus radicans Hiedra Anacardiaceae Latex of stems, branches and leaves. 3-n-pentadecyl-catechol reacts

rapidly with proteins in the skin and mucous membranes.Solanum nigrum Hierba mora Solanaceae Glycoside: solanine. Loss of sensation, bloating, diarrhea, dilated pupils.Tribulusterrestris

Torito Zygophyllaceae Seeds. Nitrates. Photosensitization.

Source: Valdez Tamez, 2002


It is urgent to considerate that the management programs of the distinct scruband grazing lands should be based on integral projects and multidisciplinaryactions, i.e. the conservation and multipurpose use of the involved resources(Sánchez and Aguirre 2004). This requires considering a complex of strategies,including:

1. Diverse livestock with management of domestic livestock and wildlifesystems.

2. Multiple uses of shrub and arboreal species employed for both forage andgoods supply (wood, firewood, fibers, wax, among others) andenvironmental services (soil protection, microclimate, landscape, urbanarboriculture, etc.).

To perform a better and proper resource management of communities of thescrub/grassland located phytogeographically in arid and semiarid zones certaindisturbance thresholds or onset indicators of disturbances should be establishedalthough they should not go beyond compromising the ability of self-restorationand autoregulation of ecosystems. The observation of these thresholds wouldresult in conservation criteria and sustainable use of the existing communities andtheir related resources such as water, soil and biotic resources.

Costly, manipulative restoration efforts are frequently required (Hobbs andNorton 1996, Whisenant 1999, Suding et al. 2004) but success of such restorationefforts is usually uncertain, and in some cases restoration may be impractical dueto financial and technical constraints. Dryland ecosystems and aquatic ecosystemsare the most-frequently cited examples of systems characterized by multiplealternative states (Rapport and Whitford 1999, Scheffer and Carpenter 2003).

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Chapter 3

STRUCTURAL ANALYSIS OF METAL-OXIDENANOSTRUCTURES

Ahsanulhaq Qurashi1, 2,*, M. Faiz2 and N. Tabet3

1School of Engineering, Toyama University, 3190 Gofuku,Toyama 930-8555, Japan

2Chemistry Department, and Center of Research Excellence inNanotechnology at, King Fahd University of Petroleum

and Minerals, Dhahran, Saudi Arabia3Physics Department, and Center of Research Excellence in Nanotechnology

at, King Fahd University of Petroleumand Minerals, Dhahran, Saudi Arabia

Abstract

Structural analysis plays the key role in understanding the relation between thepreparation and morphology and intrinsic properties of metal oxidenanostructures. In this chapter structural analysis of metal oxide nanostructuresby variety of fundamental techniques like X-ray diffraction pattern (XRD),Grazing incident X-ray diffraction (GIXRD), Scanning electron microscopy(SEM), field emission scanning electron microscopy (FESEM), transmissionelectron microscopy (TEM), atomic force microscopy (AFM), energy dispersiveX-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPES) etcwill be presented. The detailed crystal structure of In2O3 pyramids will be

* E-mail address: [email protected] Fax: +81-76-445-6734 (Author to whom correspondence

should be addressed: Ahsanulhaq Qurashi).

Ahsanulhaq Qurashi, M. Faiz and N. Tabet52

presented by XRD analysis. For in-situ examination, grazing incidence smallangle scattering and diffraction of X-rays (GIXRD) results of ZnO nanostructureswill be presented. The morphological description and the examination of thesurface of the structure, which includes the analysis of their eventual structureand their size, shape distribution, cross-section from their surface to the substrate,the analysis of the surface and its inhomogenities will be attained by SEM andFESEM analysis. Detailed structural analysis with single crystalline nature ofZnO nanostructures will be presented by TEM equipped with selected areaelectron diffraction pattern (SAED) and high resolution transmission electronmicroscope (HRTEM). The surface roughness of vertically aligned ZnO nanorodarrays will be studied by atomic force microscopy (AFM). In order to know theexact chemical composition, stiochiometry and chemical bonding nature ofmetal-oxide nanostructures, the energy dispersive X-ray spectroscopy (EDX),and X-ray electron spectroscopy (XPES) techniques will be included.

1. Introduction

The development in material science has focused interest on the propertiesand performance of materials on the scale of nanometers. Recently science hasoffered the means to maneuver materials on an atom-by-atom basis andestablished procedures for material characterization on the small size scales.These expansions are having an almost instantaneous impact on technology andsevere efforts are been made to develop them in various applications. In last twodecades, extensive interest has surfaced in the synthesis of nanoscale materials [1-12]. Metal-oxides represent an assorted and appealing class of materials withproperties which cover the entire range from metals to semiconductors andinsulators and almost all aspects of material science and physics. Metal-oxideshave a great potential in various applications due to their interesting physicalproperties, such as superconducting, semiconducting, ferroelectric, piezoelectric,pyroelectric, ferromagnetic, optical, and sensing [13-23]. Nano-scaled oxidematerials have attracted great interest in the last decade because they can exhibitdifferent physical properties than their bulk counterparts. Due to their specialshapes, compositions, chemical and physical properties, metal-oxidenanostructures are the focus of current research efforts in nanotechnology sincethey are the commonest minerals in the earth [24-40]. Metal-oxide nanostructureshave now been widely used in many areas, such as ceramics, catalysis, sensors,transparent conductive films, electro-optical and electro-chromic devices [41-45].

Structural analysis of metal-oxide nanostructures comprises the set ofphysical instruments required to study and predict the behavior, and properties ofnanostructures. To perform an accurate analysis and study the complex propertiesof metal-oxide nanostructures, nanotechnologist usually determines their

Structural Analysis of Metal-Oxide Nanostructures 53

morphology, crystal quality, chemical composition, stiochiometry, and otherrelated properties. In this chapter we present different techniques employed tocharacterize and study the behavior of metal- oxides at nanoscale.

2. Structural Characterization of Metal-OxideNanostructures

Structural analysis of metal-oxide nanostructures is carried out by variety oftechniques includes XRD, GIXRD, SEM, FESEM, EDX, TEM, AFM, and XPES.

2.1. XRD

The X-ray diffraction is knowledge of determining the arrangements of atomswithin crystal from the manner in which a beam of X-ray is scattered from theelectrons with in the crystals. The discovery of X-rays in 1895 enabled scientiststo probe crystalline structure at the atomic level. X-ray diffraction has been in usein two main areas, for the fingerprint characterization of crystalline materials andthe determination of their structure. Each crystalline solid has its uniquecharacteristic X-ray powder pattern, which may be used as a "fingerprint" for itsidentification. Once the material has been identified, X-ray crystallography maybe used to determine its structure, i.e. how the atoms pack together in thecrystalline state and what the inter-atomic distance and angle are etc. X-raydiffraction is one of the most important characterization tools used in solid-statechemistry and materials science. We can determine the size and the shape of theunit cell for any compound most easily using the diffraction of X-rays. Thedistance between similar atomic planes in mineral called d-spacing is measured inangstroms. The angle of diffraction known as theta angle and is measured indegrees. For practical reasons the diffractometer measures an angle twice that ofthe theta angle. Not surprisingly, we call the measured angle '2-theta'. Thewavelength of the incident X-radiation, symbolized by the Greek letter lambdaand, in our case, equal to 1.54 angstroms. These factors are combined in Bragg’slaw, which is explained below.

The path difference between two waves:

2 x= 2dsin(θ) (2.1)

For constructive interference between these waves, the path difference mustbe an integral number of wavelengths:


n x λ = 2x (2.2)

This leads to the Bragg’s equation:

n x λ = 2dsin(θ) (2.3)

This equation was proposed by physicists Sir W.H. Bragg and his son SirW.L. Bragg in 1913 to explain why the cleavage faces of crystals appear to reflectX-ray beams at certain angles of incidence (theta,θ). The variable “d” is thedistance between atomic layers in a crystal, and the variable lambda is thewavelength of the incident X-ray beam while n is an integer [46]. The X-raydiffraction (XRD) patterns were recorded on a Rigaku, D/max 2500diffractometer. The X-ray source of this diffractometer emits Cu-Ka radiationwith wavelength of 1.5418 Å. The diffractometer is calibrated with reference tostandard oriented Si wafer. For usual structural phase analysis, a scan ratebetween 4- 8 º per min. was used. XRD is first and foremost technique to be usedto know the phase, crystal structure and purity of materials. The XRD spectrum ofIn2O3 nanostructures shown in Figure 1. All the XRD peaks are indexed to thepure cubic phase with lattice parameter a = 1.011Å (JCPDS No. 89–4595),indicating that pure phase [47]. However, no any other impurities existed in thesample.

Figure 1. XRD spectra of In2O3 pyramids. Reprinted from Ref. [47 ], with permissionfrom copyright 2010 Elsevier.


Figure 2. GIXRD of ZnO nanostructures with various deposition times (a = 0.48)Reprinted from Ref. [48 ], with permission from copyright 2010 Elsevier.

2.2. Gixrd

Grazing incidence X-ray diffraction is an excellent technique to analyzecrystal phase of material compared to the XRD. The penetration depth of X-raysis often found in the 10-100 µm range. In most thin film investigations thethickness is substantially lower causing a large fraction of the diffractogram asmeasured in the symmetric θ/2θ configuration to stem from the substrate.Especially for the analysis of thin films X-ray diffraction techniques have beendeveloped for which the primary beam enters the sample under very small anglesof incidence. In its simplest variant this configuration is denoted by GIXRD thatstands for grazing incidence x-ray diffraction. The small entrance angle causes thepath traveled by the x-rays to significantly increase and the structural informationcontained in the diffractogram to stem primarily from the thin film. The GIXRDspectrum of ZnO nanostructures grown for different deposition time wasillustrated in Figure 2 [48]. All the GIXRD peaks indicated wurtzite hexagonalcrystal structure of ZnO nanostructures. The sharp peaks particularly (1 0 0) and(1 0 1) peaks of the ZnO nanorods indicate good crystal quality. With the help ofGIXRD measurement, it was found that both these peaks increase with thedeposition time, which enhanced the crystallinity as well as the quantity ofnanorods. Also full width half maximum (FWHM) was calculated by usingGIXRD results.


Figure 3. (a) Low magnification and (b) high magnification SEM images of the ZnO NWarrays grown on a GaN substrate using a hydrothermal approach. Reprinted from Ref.[51], with permission from copyright 2010 American Chemical Society.

2.3. SEM and FE-SEM

Scanning electron microscopy and field emission scanning electronmicroscopy techniques are commonly used to find out morphological details ofnanostructures, their surfaces and size respectively. Field-emission cathode in theelectron gun of a scanning electron microscope provides narrower probing beamsat low as well as high electron energy, resulting in both improved spatialresolution and minimized sample charging and damage. The scanning electronmicroscopy (SEM) was pioneered by Manfred von Ardenne in the 1930s [49, 50].SEM can scan the surface of a sample with a finely focused electron beam toproduce an image when the electrons hitting the surface are reflected and aredetected by a fluorescent screen or computer monitor. The instrument providesresolution of 1 nm at 30 kV and 4 nm at 1.0 kV. SEM images of ZnO nanorodarrays (NRAs) grown on GaN substrate is illustrated in Figure 3 [51]. The


morphology, length and top view surface can be easily observed from the SEM ofZnO NRAs. The field-emission scanning electron microscope (FESEM) uses aSchottky field-emission source for electrons. The Schottky emitter combines withthe high brightness and low energy spread of the cold field emitter that have thehigh stability and low beam noise of thermal emitters. The instrument is equippedwith about 100 times the emitting area as a cold emitter, and as a result, has muchhigher probe currents that give it an advantage for analytical applications. Theelectron gun is directly linked to a beam booster. This linkage eliminates, amongother things, crossover of electrons along the beam path, which aids in operatingat very low beam energies. The field-emission scanning electron microscope isequipped with two secondary electron detectors: below-lens for low-resolutionwork and in-lens for high-resolution imaging. The backscattered electron detectoris a solid state detector and is optimized for short working distances. The fourdiodes of the detector are designed for both low- and high-accelerating voltageoperations.

This instrument may be applied to several areas of research, includingmaterials science, nanotechnology, geology, and biology. For FESEMobservation, the samples are first made conductive. The samples were coated withan ultrathin layer (1.5-3.0 nm) of gold, gold-palladium or platinum. After that, theobjects must be able to sustain the high vacuum and should not alter the vacuum,for example by losing water molecules or gasses. For the structural analysis, asmall piece of the substrate, which contains the deposited products, was pasted onthe sample holder using the carbon tape. The object is inserted through anexchange chamber into the high vacuum part of the microscope and anchored on amoveable stage. The object can be moved in horizontal and vertical direction, andits position can be changed in the chamber left-right axis, or forward andbackward. In addition, the object can be tilted, rotated and moved. FESEM givesus morphological features, size and surface at high-magnification mostly less than100 nm.

Figure 4 shows plane and 45° tilted view images of hexagonally patternedZnO NRAs [52]. From the tilted view image one can find out the diameter, lengthand surface of the sample. This is the most convenient method to know thevertical alignment of 1D nanostructures with the substrate. Figure 5 shows topview low and high magnification FESEM images of In2O3 nanopushpins [53].With the help of high-magnification FESEM images we can see the clear view,smoothness and geometry of In2O3 nanopushpins and their pyramidal tips. Inorder to know precisely the exact thickness, diameter, and length ofnansotructures grown on the substrate, traditionally rm cross-section FESEManalysis of nanostructures will be carried out.


Figure 4. (a) and (b) Low magnification FESEM image of ZnO NRAs grown in hexagonalpatterns with the size of 15 and 10m, respectively; (c) and (d) represent their magnifiedFESEM images; and (e) and (f) high magnification 45◦ tilted FESEM image taken fromthe center of hexagon and circle patterns, respectively. Reprinted from Ref. [52], withpermission from copyright 2010 Elsevier.

Figure 5. (a) and (b) low and high magnification FESEM images of In2O3 nanopushpinsReprinted from Ref. [53], with permission from copyright 2010 American institute ofphysics.


Figure 6. SEM images of ZnO nanorods grown on Zn/Si substrate (a) top view, (b) tiltview, (c,d) edge tilt view. Center photograph (inset) demonstrates uniform ZnO nanorodgrowth on a 4-in. Si wafer. Reprinted from Ref. [54], with permission from copyright 2010American Chemical Society.

Figure 6 shows the cross-section FESEM analysis of ZnO NRAs grown on Zn-coated silicon substrate [54]. The alignment of ZnO nanowires with the substratecan be easily seen and their thickness/length can be obtained by this method.However as compared to tilted and surface view sample preparation for cross-section is somewhat difficult.

2.4. TEM

Transmission electron microscopy (TEM), is an imaging technique whereby abeam of electrons is transmitted through a specimen, then an image is formed,magnified, and directed to appear either on a fluorescent screen or layer of or tobe detected by a sensor such as a CCD camera The first practical transmissionelectron microscope was built by Albert Prebus and James Hellier at theUniversity of Toronto in 1938 using concepts developed earlier by Max Knoll andErnest Ruska [55]. TEM uses electrons as “light source” and their much lowerwavelength make it possible to get a resolution a thousand times better than with alight microscope. A schematic diagram of transmission electron microscopy isshown in Figure 7. A "light source" at the top of the microscope emits theelectrons that travel through vacuum in the column of the microscope. Instead of


glass lenses focusing the light in the light microscope, the TEM useselectromagnetic lenses to focus the electrons into a very thin beam. The electronbeam then travels through the specimen. Depending on the density of the materialpresent, some of the electrons are scattered and disappear from the beam. At thebottom of the microscope the unscattered electrons hit a fluorescent screen, whichgives rise to a "shadow image" of the specimen with its different parts displayedin varied darkness according to their density. The image can be studied directly bythe operator or photographed with a camera. One can see objects to the order of afew angstrom (10-10 m). The possibility for high magnifications has made theTEM a valuable tool in both medical biological and materials research. For TEManalysis, the nanostructures were ultrasonically dispersed from the substrate inacetone and a drop of acetone, which contains the nanostructures, was placed on acarbon coated copper TEM grid and examined.

Figure 7. Schematic diagram of transmission electron microscopy.


Figure 8. (a) TEM, (b) SAED, and (c) HR-TEM images of ZnO NR gown on ZnO/ Sisubstrate. Reprinted from Ref. [20], with permission from copyright 2010 Americaninstitute of physics.

Figure 9. TEM image of the flower-shaped ZnO structure grown for 9 h (a) and itscorresponding SAED pattern (b). Reprinted from Ref. [10], with permission fromcopyright 2010 Elsevier.


By using transmission electron microscopy analysis detailed structuralinformation can be obtained. Figures 8 (a-c) represent a transmission electronmicroscopy [TEM], selected area electron diffraction (SAED), and high-resolution (HRTEM) images of single ZnO nanorod respectively [20]. Theseresults reveal that the as-grown nanorods are fairly single crystalline and arecrystallized hexagonally along the [001] direction with homogeneous geometry. Itis possible to know the crystal plane of and their corresponding spots in SAED byusing camera constant. Also by HRTEM, we can find out the lattice parametersand growth axis very easily as shown in Figure 8. Figure 9 shows another typicalexample of TEM analysis of large sized flower shaped structures withcorresponding SAED pattern [10]. Each petal of the flower-shaped structuresshows a single crystalline nature and grown along the c-axis in [0002] direction

2.5. EDX

Energy dispersive X-ray spectroscopy (EDS or EDX), is an analytical toolpredominantly used for chemical characterization. Being a type of spectroscopy, itrelies on the investigation of a sample through interactions between light and matter,analyzing X-ray in its particular case. Its characterization capabilities are due inlarge part to the fundamental principle that each element of the periodic table has aunique electronic structure and, thus, a unique response to electromagnetic waves.This EDX system is either attached with SEM/FESEM or TEM.

Figure 10. Shows the EDX analysis of MgO nanoflakes. Reprinted from Ref. [56], withpermission from copyright 2010 Elsevier.


Figure 10 shows EDX spectrum of MgO nanoflakes [56]. In this case the EDX wasattached with SEM. The EDX analysis showed the chemical composition ofmagnesium and oxygen with an atomic ratio of approximately 1:1, which is in agood agreement with the stoichiometric ratio of MgO. To know the appropriatechemical composition of individual nanostructure, EDX equipped with TEM isusually performed. Figure 11 illustrates the EDX spectrum of single In2O3 nanowiretaken during the TEM analysis [57]. The EDX spectrum in Figure 10 shows that thesingle crystalline nanowire composed of only In, O and Au respectively.

2.6. AFM

The atomic force microscope (AFM) is one kind of scanning probemicroscopes (SPM). SPM are designed to measure local properties, such as height,friction, and magnetism with a probe. To acquire an image, the SPM raster scans theprobe over a small area of the sample, measuring the local property simultaneously.The atomic force microscope (AFM), or scanning force microscope (SFM), is avery high-resolution type of scanning probe microscopy, with demonstratedresolution of fractions of a nanometer, more than 1000 times better than the opticaldiffraction limit. The AFM was invented by Bining, Quate and Gerber in 1986, andis one of the foremost tools for imaging, measuring, and manipulating matter at thenanoscale [58]. The AFM consists of a microscale cantiliver with a sharp tip (probe)at its end that is used to scan the specimen surface. A shematic diagram of AFM isshown in Figure 12. The cantilever is typically silicon or silicon nitride with a tipradius of curvature on the order of nanometers.

Figure 11. EDX spectrum for In2O3 nanowire, Reprinted from Ref. [57], with permissionfrom copyright 2010 Elsevier.


Figure 12. Schematic diagram of atomic force microscopy.

Figure 13. AFM images of Al-doped ZnO film on (a) a Si wafer and (b) a PET substrate.Reprinted from Ref. [59], with permission from copyright 2010 American chemicalSociety.

When the tip is brought into proximity of a sample surface, force between the tipand the sample lead to a deflection of the cantilever according to Hook’s law.Depending on the situation, forces that are measured in AFM include mechanicalcontact force, van der Wall forces, capillary forces, chemical bonding,electrostatic forces, magnetic forces, casimir forces, and solvation forces etc.Figure 13 (a) shows AFM image of small islands on Si substrate which serve asnuclei for the growth of ZnO nanorods. Two substrates were compared in terms of


uniformity. In Figure 13(b) we see the PET surface does not have uniform islandsize and distribution compared to that of Si substrate [59].

The AFM 3D images are shown in Figure 14 that represents (a) ZnOnanorods grown in a single strip pattern and (b) the surface of the as-grownnanorods within the strip. An average rms surface of the nanorods is found to be39 nm [60]. The as-grown ZnO nanostructures with firm adhesion to the substrateof sufficiently minimal topography are easily classified. This adhesion of alignednanorods to the surface is to guarantee that the probe tip does not push the samplearound during imaging.

Figure 14. (a and b) AFM images ZnO nanorods grown on strip patterns. Reprinted fromRef. [60].


2.7. XPES

X-ray Photoelectron Spectroscopy (XPES) is a quantitative spectroscopicsurface chemical analysis technique used to estimate the empirical formula orelemental composition, chemical state and electronic state of the elements on thesurface (upto 10 nm) of a material X-Ray irradiation of a material under ultra highvacuum (UHV) leads to the emission of electrons from the core orbitals of the top10 nm of the surface elements of the material being analyzed.

1s core-level spectrum and two O 1s peaks resolved by using curve-fitting procedure.Reprinted from Ref. [63], with permission from copyright 2010 Elsevier.

Figure 15. (a) Wide XPS survey scan spectra of the as-grown nanobat-like CuOnanostructures; (b) the peaks attributed to the Cu 2p3/2 and Cu 2p1/2; and (c) the O.


Measurement of the kinetic energy (KE) and the number of electrons escapingfrom the surface of the material gives the XPS spectra. From the kinetic energy,the binding energy of the electrons to the surface atoms can be calculated. Thebinding energy of the electrons reflects the oxidation state of the specific surfaceelements. The number of electrons reflects the proportion of the specific elementson the surface. XPS is used to measure elements that contaminate a surface,uniformity of elemental composition across the top surface and uniformity ofelemental composition as function of ion beam etching.

XPS can also be used to analyze the change in surface chemistry of a materialafter chemical or physical treatments such as: leaching, reduction, fracture, cuttingor scraping in air or UHV to expose the bulk chemistry, ion beam etching to cleanoff some of the surface contamination, exposure to heat to study the changes dueto heating, exposure to reactive gases or solutions, exposure to ion beam implant,exposure to UV light, etc. XPS is used to determine; 1) what elements and thequantity of those elements that are present within ~10 nm of the sample surface;2) what contamination, if any, exists in the surface or the bulk of the sample; 3)empirical formula of a material that is free of excessive surface contamination; 4)the chemical state identification of one or more of the elements in the sample; 5)the binding energy (BE) of one or more electronic states; 6) the thickness of oneor more thin layers (1–8 nm) of different materials within the top 10 nm of thesurface. To count the number of electrons at each KE value, with the minimum oferror, XPS must be performed under UHV conditions because electron countingdetectors in XPS instruments are typically one meter away from the materialirradiated with X-rays. As the energy of a particular X-ray wavelength used toexcite the electron from a core orbital is a known quantity, we can determine theelectron binding (BE) of each of the emitted electrons by using an equation that isbased on the work of Ernest Rutherford (1914):

Ebinding = Ephoton- Ekinetic – Φ (2.4)

where Ebinding is the energy of the electron emitted from one electron configurationwithin the atom, Ephoton is the energy of the X-ray photons being used, Ekinetic is thekinetic energy of the emitted electron as measured by the instrument and Φ iswork function of the spectrometer (not the material) [61,62].

A typical XPS spectrum is a plot of the number of electrons detected (Y-axis,ordinate) versus the binding of the electrons detected (X-axis). Each elementproduces a characteristic set of XPS peaks at characteristic binding energy valuesthat directly identify each element that exist in or on the surface of the materialbeing analyzed. These characteristic peaks correspond to the electron


configuration of the electrons within the atoms, e.g., 1s, 2s, 2p, 3s, etc. Thenumber of detected electrons in each of the characteristic peaks is directly relatedto the amount of element within the area (volume) irradiated. To generate atomicpercentage values, each raw XPS signal must be corrected by dividing its signalintensity (number of electrons detected) by a "relative sensitivity factor" (RSF)and normalized over all of the elements detected. The main components of XPSsystem include; 1) A source of X-rays; 2) An ultra high vacuum (UHV) stainlesssteel chamber with UHV pumps; 3) An electron collection lens; 4) An electronenergy analyzer; 5) Mu-metal magnetic field shielding; 6) An electron detectorsystem. Typically, the diameter (diagonal) of the sample should be <22.0mm, andits thickness should be <3.5 mm. For preparing XPS samples, the samples and thesample holders should be handled with clean tweezers and gloves. Using theoptimized procedure, metal-oxide nanostructure samples can be characterized.The phase purity, chemical state and chemical composition of CuO nanobat-likestructures was investigated by XPES [63]. Figure 15 (a) illustrates wide surveyscan spectra of the CuO nanostructures and only CuO and C peaks could be seen.The peaks related to Cu 2p3/2 and Cu2p1/2 is shown in figure 15 (b). However infigure 15 (c) the O 1s core-level spectrum is broad, and two O 1s peaks can beresolved by using curve-fitting procedure. Using the fitting procedure anysuppressed peak can be resolved to know it chemical nature and origin. Also usingXPES technique we can easily find out the stiochiometry of nanomaterials.

3. Summary

Structural analysis of metal-oxide nanostructures includes the set of physicalinstruments required to study and predict the behavior, and properties ofnanostructures.

The typical example of In2O3 pyramids in terms of XRD mentioned. For in-situ examination, grazing incidence small angle scattering and diffraction of X-rays (GIXRD) of ZnO nanstructures were included. The morphologicaldescription and the examination of the surface of the ZnO and In2O3

nanostructures, which includes the analysis of their eventual structure and theirsize, shape distribution, cross-section from their surface to the substrate, theanalysis of the surface and its inhomogenities preseneted by SEM and FESEManalysis. Detailed structural analysis with single and polycrystalline nature ofZnO nanorod-like structure and flower-like nanostructures presented by TEMequipped with selected area electron diffraction pattern (SAED) and highresolution transmission electron microscope (HRTEM). The surface roughness of


well-aligned and strip patterned ZnO NRAs, on lattice matching substrates werestudied by AFM. The exact chemical composition, surface functionality,stiochiometry and chemical bonding nature of MgO, In2O3 and CuOnanostructures, studied by EDX, and XPES techniques .

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Chapter 4

A METHOD FOR SUPPORTING EFFORTSTO IMPROVE EMPLOYEE MOTIVATION

USING A COVARIANCE STRUCTURE

Yumiko Taguchi 1, Daisuke Yatsuzuka 2

and Tsutomu Tabe2

1Department of Business Administration and Communication, ShohokuCollege, 428 Nurumizu, Atsugi, Kanagawa 243-8501 Japan

2Department of Industrial and Systems Engineering, College of Science andEngineering, Aoyama Gakuin University, 5-10-1 Fuchinobe, Sagamihara,

Kanagawa 229-8558 Japan

Abstract

This study elucidates a method for showing the priority of factors used togenerate motivation in companies, in order to support efforts to improveemployee motivation. We verify the effectiveness of the method by aquestionnaire survey. This study proceeds towards the above-describedobjectives in the following three steps. Step 1: Discussing the factors whichconstitute employee motivation in the literature. Step 2: Contriving a method toset the priority of improvement-forming factors by using covariance structureanalysis and graphical modeling. Step 3: Verifying the method proposed in thisstudy based on results obtained. We had the employees at a restaurant fill out aquestionnaire by the method proposed in this study. We confirmed that theimprovement priority was effective as a support for improvement.

Yumiko Taguchi, Daisuke Yatsuzuka and Tsutomu Tabe74

1. Introduction

According to the Ministry of Economy, Trade and Industry, there have beenrecent increases in both the numbers of Japanese companies launching overseasoperations and the numbers of foreign companies moving into Japan [1] [2]. Withthe globalization of business, Japanese companies are more likely to diversifyoverseas and overseas companies are more likely to expand into Japan. Withinthis environment, the competition between Japanese companies and overseascounterparts is expected to increase. Numerous social trends in Japan posechallenges to the competitiveness of Japanese companies, including workforcereductions due to the low birthrate [3], job hopping by younger workers [4], lossof personnel as baby-boomers reach the age of mandatory retirement [5], andgeneral declines in the work ethic and levels of energy at the workplace [4].Within this social context, companies must improve their levels of performancewith limited workforce. If they are to succeed, their employees must be motivated.The effects of motivation on productivity are tremendous. Low motivationreduces employee performance to only 20-30% of the actual capacity of theemployees, whereas high motivation boosts performance to 80-90% [6]. Tooptimize the work efficiency of employees, a management team should raiseemployee motivation and maintain it at high levels.

Theories in conventional studies on motivation include the two-factor theoryof emotion[7]，the ERG theory [8], and the Need-Hierarchy Theory [9]. All threetheories focus strictly on the mechanism or structure of motivation. As such, noneof them help us concretely identify the specific measures required to improvemotivation in an individual company. They do little to improve employeemotivation or equip companies with strategies for responding adeptly to therequests of their employees on a fulltime basis.

In this study we elucidate a method for identifying the priority ofimprovements used to enhance employee motivation. After introducing themethod itself, we report the results of a questionnaire survey used to verify theeffectiveness of the method.

2. Problem-Solving Approach

This study proceeds towards the above-described objectives in the followingthree steps. Step 1: Discussing the factors which constitute employee motivation in theliterature. 2) Contriving a method to set the priority of improvement-forming factors.Step 3) Verifying the method proposed in this study based on results obtained.

A Method for Supporting Efforts to Improve Employee Motivation… 75

2.1. Discussing the Factors which form Motivation

To identify motivation-forming factors for this study, we first need toconsider the motivations of employees working for companies. Herzbergproposed a motivation-hygiene theory based on interview surveys withemployees, hypothesizing a framework of factors which contribute to jobsatisfaction (motivator factors) combined with factors which contribute to jobdissatisfaction (hygiene factors). Both motivator factors and hygiene factors havean important bearing on motivation. Next, Herzberg went on to describe anothereight factors deeply related to motivations in this theory: evaluation, humanrelationships, shop, working conditions, achievement, pay, the work itself, andgrowth. Here, we treat these eight factors as the eight major motivation-formingfactors in the present study. We also establish specific items related to the eightfactors to allow us to easily specify improvements of the factors. In doing so, weassume that the status of the motivation-forming factors contributes to employeesatisfaction. Through our discussion on this approach, we derive the motivationformation factors shown in Figure 1 below (cause-and-effect diagram).

Figure 1. motivation-forming factors in this study.


There are eight major motivation-forming factors (hereinafter referred to as“motivation factors”), i.e., income, growth, establishment, evaluation, work, shop,working conditions, and human relationships. Each motivation factor consists ofminor factors (i.e., sub-factors). “Income” consists of three sub-factors: payappropriate for ability, bonus, and overtime pay. “Growth” consists of five sub-factors: promotion system, educational system, position and role, promotionspeed, and improvement of job abilities. “Establishment” consists of three sub-factors: manifestation of target, establishment of the target, and contribution tocompany. “Evaluation” consists of three sub-factors: evaluation by supervisor,evaluation by co-worker, and evaluation by customer.

“Work” consists of four sub-factors: difficulty of work, discretion of work,interesting work, and satisfying work. “Shop” consists of two sub-factors: vitalityof the workplace and cooperation between departments. “Working conditions”consist of three sub-factors: work volume, quota, and working hours. “Humanrelationships” consist of four sub-factors: relationship with supervisor,performance of supervisor, relationship with co-workers, and performance of co-workers.

2.2. Contriving a Method to Induce Improvement Priority

In this study, we assume that the factors with stronger influence on employeesatisfaction will need to be improved in order to improve their priority against themotivation-forming factors set in 2.1 above. In measuring this influence, we needto consider the following three points as the characteristics of the motivation-forming factors.

I. In a preliminary survey, we surveyed the level of employee satisfactionassociated with each of the motivation factors covered in the employeequestionnaire. The employees surveyed indicated that it was difficult toimagine motivation factors and answer questions about them. Thus, itwas difficult to observe the state of the eight motivation factorsquantitatively.

II. It is necessary to set model freely because companies, departments andworkplaces have different factors to form motivation.

III. The motivation factors extracted from the motivation-formation factorsare intricately interrelated. Satisfaction with human relationships, forexample, affects the level of satisfaction with work. Additionally,satisfaction with the state of the work affects the levels of satisfaction


with both growth and establishment. These relationships can be set byempirically. The settings, however, may vary between setters. The settingdoes not necessarily reflect the situation of motivation of employees.

To realize items I and II above, we apply a covariance structure analysis [10]that analyzes the relationships between factors quantitatively. The total effect ofeach factor is calculated from the path coefficient between factors obtained fromthe analysis results. This is used as the level of influence on satisfaction degree ofemployees for each factor.

Additionally, we use a graphical modeling [11] capable of elucidating andorganizing the relationship between motivation factors in an objective andexploratory manner in order to construct a model for covariance structureanalysis, in consideration of item III shown above. Data on the state of factors arecollected directly from the opinions of employees and the results of thequestionnaire survey on the employee’s level of satisfaction with work.

Graphical model shown above will be explained in the next section. Thespecific method to prioritize the improvement items is described in chapter 3.

2.3. Use of Graphical Modeling

Graphical modeling is a method to arrange relationships between variables byobtaining partial correlation coefficients, as shown in Figure 2.

Figure 2. Graphical modeling.

The relationships are derived from data, not from models constructed ashypotheses. In this study, we use graphical modeling to arrange relationshipsamong eight motivation factors.


A partial correlation coefficient is obtained from a simple correlationcoefficient among variations. Specifically, we obtain a partial correlationcoefficient from equation (1) by expressing the correlation matrix as

)( ijrR = ，and the inverse matrix as )(1 ijrR =− . If a partial correlationcoefficient is 0, there is no relationship between the variables.

jjii

ij

restijrr

rr⋅

−=⋅

(1)

3. Method to Derive the Priority of MotivationImprovements

Based on 2.2 shown above, we calculate the priority of motivationimprovements in this study in six steps, as shown in Figure 3.

Specifically, 1) Setting variables for the analytic procedure, 2) carrying outthe questionnaire survey on the level of employee satisfaction with work, 3)understanding a simple correlation coefficient between motivation factors byconfirmatory factor analytic procedure, 4) arranging the relationships betweenmotivation factors by graphical modeling, 5) constructing a model and performinga covariance structure analysis, 6) calculating the influence of each sub-factor ontotal employee satisfaction, and calculating the improvement priority.

3.1. Handling the Variables in this Study

Three variables are used for the confirmatory factor analysis and covariancestructure analysis in this study: a latent variable, an observed variable, and anerror variable.

Eight hardly observable motivation factors are set as latent variables.An observed variable uses the following two satisfaction levels as

quantitatively obtained data. The first is the level of satisfaction with 27 sub-factors out of the motivation-formation factors. The second is the level ofemployee satisfaction with work as a whole.

There is assumed to be a causality between the latent variable and observedvariable, as shown in Figure 4.


Figure 3. Method to derive the priority of improvements.

Figure 4. Variables used in this study, and the relationships among them.


The error variable is a constructive concept that assembles effects of anyfactor not expressed by the model. In this study, error variables are established forboth the observed variables and latent variables.

If the error variable has a considerable impact on the observed variable andlatent variable, there are likely to be other influences that the model cannotexpress. In this case, the model cannot explain the relationship.

3.2. Questionnaire on Satisfaction with Work

The questionnaire is roughly divided into surveys on two items: 1) the levelof satisfaction with work as a whole and 2) the level of satisfaction with the sub-factors.

2) above are data for observed variables.The satisfaction level was evaluated on a 6-point scale. Options 1-3 denote

dissatisfaction. Options 4-6 denote satisfaction.As an example, Figure 5 shows the questionnaire format for the sub-factor

“Evaluation.”

Figure 5. Questionnaire format for “Evaluation”.

3.3. Understanding a Simple Correlation Coefficient betweenMotivation Factors by Confirmatory Factor Analysis

A confirmatory factor analysis, can freely construct and verify therelationships among conceivable factors (latent variable) and observed variables,and factors. In this study, we perform a confirmatory factor analysis to confirmwhether or not there is correlation between the eight motivation factors.


Figure 6. A model used in confirmatory factor analysis.

We perform the analysis by setting up the model shown in Figure 6. Themodel assumes that there is co-variation between all latent variables and a causalrelationship between latent variables and observed variables. The analysis isconducted with Amos software for multivariate analysis. The simple correlationcoefficients between motivation factors are obtained. If we find, when focusing ona characteristic factor, that a simple correlation coefficient value with anothercharacteristic is significantly low, we conclude that the characteristic factor offocus has no relationship with the other motivation factors. In other words, it isdetermined to be a non-correlated factor and removed from the target factors forgraphical modeling.


3.4. Arranging the Relationships Between MotivationFactors by Graphical Modeling

Based on the simple correlation coefficient between motivation factorsobtained by the confirmatory factor analysis in the previous section, we obtainpartial correlation coefficients between motivation factors using the graphicalmodeling described above. Motivation factors with partial correlation coefficientsof zero are assumed to be unrelated.

3.5. Constructing a Model and Performing a Covariance StructureAnalysis

At first, we use construct a model expressing the structure of motivation-formation factors in consideration of the relationships between motivation factorsobtained by the graphical modeling described above. Arrows are used to illustratethe causal relationships (time relationships) between the motivation factors whichare determined to have relationships. Next, the Amos software for multivariateanalysis is used to conduct a covariance structure analysis based on the model.

3.6. Calculating the Influence of Each Sub-Factor on the total Levelof Satisfaction, and Calculating the Improvement Priority

The influence of each motivation formation factor on the total level ofsatisfaction is obtained by the following three steps.

Step 1). Based on the path coefficient obtained from the covariance structureanalysis for each observed variable, obtain the product of the pathcoefficients of the observed variable from the total satisfaction level.

Step 2). Compute the total sum (hereinafter referred to as the “total effect”) ofthe products of the path coefficients to determine the improvementpriority, including the direct effect and indirect effect. Figure 7 is anexample of a calculation of the total effect of sub-factors, the“establishment of a target,” and “cooperation.” The sub-factor“cooperation” shows indirect effect from the total satisfaction level viamotivation factors “establishment.”

Step 3). As a result of step 2 above, factors with total sums have higherimprovement priority.


4. Discussion of Effectiveness

The effectiveness of the methodology proposed in this study is verified in thefollowing three steps.

Step 1. Issue a questionnaire to employees of an existing company.Step 2. Apply the method proposed in this study to the questionnaire data

shown above to generate improvement priority for motivation-formationfactors.

Step 3. Compare the generated improvement priority to a model in whichrelationships between motivation factors are not considered from theviewpoint of the goodness of fit of the model.

Figure 7. An example of calculating the influence of each sub-factor on the total satisfaction.

4.1. Questionnaire Survey

Questionnaires were issued to a restaurant. The hours of business were 17:00to 24:00, 365 days a year.

The subjects were 31 employees of the restaurant. The questionnaire wasconducted from November 21 to December 5, 2008. The contents of thequestionnaire are shown in 3.2.


4.2. Generating the Improvement Priorityfor Motivation-Formation Factors

Based on the questionnaire response data, we obtained the improvementpriority by the method described in chapter 3. The results are shown below.

4.2.1. Results of Confirmatory Factor Analysis and Graphical Modeling

Table 1 shows correlations of the latent variables (i.e., eight motivationfactors) determined via the conformity factor analysis. As shown in Table 1, thesimple correlation coefficient between shop and other motivation factors (factorsother than income) is very small. There seems to be little relationship. Thus, wesought to determine partial correlation coefficients by graphically modeling therelationships of the seven motivation factors other than “shop.” The table2 showsthe partial correlation coefficients.

Table 1. Simple correlation coefficients between latent variables

Table 2. Partial correlation coefficients between latent variables.


As shown in Figure 8, six relationships between latent variables are observedas a result of the partial correlation coefficients: income and working conditions,work and human relationships, work and work condition, work and growth,establishment and evaluation, and establishment and growth.

Figure 9 is a model of motivation formation in this study considering sixrelationships. Motivation factors with confirmed relationships in the graphicalmodeling are shown in boldface.

Figure 8. Organized relationship among latent variables.

Figure 9. Model of this study.


4.2.2. Conducting a Covariance Structure Analysis and Calculating theImprovement Priority

The model set up in the previous section 4.2.1 was expressed on AMOS andanalyzed. Figure 10 shows the results. The numbers are path coefficients betweenvariables.

Based on the result shown above, table 3 shows the total effect andimprovement priority by sub-factor.

The three major factors are work volume, establishment of the target, andworking hours.

Figure 10. Result of covariance structure analysis in this study.


Table 3. Result of calculation of improvement priority

4.3. Comparison with a Model that Disregards Relationshipsamong Motivation Factors

Next, we conducted a covariance structure analysis that disregards therelationships between motivation factors. Figure 11 shows the results. Table 4shows the improvement priority calculated based on the results.

We compared the model constructed by the method proposed by this study(i.e., a model considering the relationships between motivation factors) to a modelthat disregards the relationships between motivation factors. Figure 12 shows theresults. Compared items consist of GFI and AGFI showing the goodness of fit ofthe model and the improvement priority.

GFI and AGFI are rather high in the model of this study, as no relationshipsbetween motivation factors are modeled. Therefore, the model of this study hasvalidity.


In comparing the improvement priorities between the two models, we findthat the improvement with the highest priority differed. The top priorityimprovement in the model disregarding the relationships between motivationfactors was “satisfaction of work.” We asked for the opinions of the employee ofthe company where the questionnaire was conducted.

Figure 11. Result of covariance structure analysis disregarding the relationships betweenmotivation factors.

Some respondents commented as follows: “There is a problem with the workvolume. There is little problem, however, in the level of ‘satisfaction of work.’The improvement priority in consideration of the relationships betweenmotivation factors reflects the current situation better.” Therefore, theimprovement priority in consideration of the relationships between motivationfactors mentioned in this study is more reliable.


Table 4. Improvement priority disregarding the relationshipsbetween motivation factors

Figure 12. Comparison between the model disregarding motivation factors and this study.


5. Result

We had the employees at a restaurant fill out a questionnaire by the methodproposed in this study. We confirmed that the improvement priority was effectiveas a support for improvement.

As shown above, we elucidated a method to propose improvement priority inorder to support the efforts of managers in a company to improve the motivationof their employees.

In the future, we will need to collect data from categories of business otherthan the restaurant business and discuss whether the improvement priority iseffective in those categories.

References

[1] Ministry of Economy, Trade and Industry. (2009). Survey of OverseasBusiness Activities. Retrieved May 25, 2010, fromhttp://www.meti.go.jp/english/statistics/tyo/kaigaizi/index.html.

[2] Ministry of Economy, Trade and Industry. (2009). Survey of Trends inBusiness Activities of Foreign Affiliates. Retrieved May 25, 2010, fromhttp://www.meti.go.jp/english/statistics/tyo/gaisikei/index.html.

[3] Health, Labour, & Welfare Ministry. (2005). White paper on the LabourEconomy 2005 Summary. Retrieved May 25, 2010, fromhttp://www.mhlw.go.jp/english/wp/l-economy/index.html.

[4] Nomura Research Institute, Ltd. (2005). Regeneration of motivation forwork is important for management strategy in 2010 (in Japanese).Retrieved May 25, 2010, fromhttp://www.nri.co.jp/news/2005/051205.html.

[5] Taro Saito. (2005). Effect of retirement of baby boomers on the labormarket (in Japanese). NLI Research Institute report, 98, 8-13.

[6] Paul Hersey, Kenneth, H. & Blanchard, Dewey, E. Johnson. (1996).Management of Organizational Behavior: Utilizing Human Resources(Seventh Edition). New Jersey: Prentice-Hall, Inc., 10-11.

[7] Frederick Herzberg, Bernard Mausner, Barbara Bloch Snyderman. (1993).The Motivation to Work. New Jersey: Transaction Publishers.

[8] Paul Hersey, Kenneth, H. & Blanchard, Dewey, E. Johnson. (1996).Management of Organizational Behavior: Utilizing Human Resources(Seventh Edition). New Jersey: Prentice-Hall, Inc., 40-45.

[9] Paul Hersey, Kenneth, H. & Blanchard, Dewey, E. Johnson. (1996).


Management of Organizational Behavior: Utilizing Human Resources(Seventh Edition). New Jersey: Prentice-Hall, Inc., 45.

[10] Jöreskog, K. G. (1978). Structural analysis of covariance and correlationmatrices. Psychometrika, 43(4), 43-477.

[11] Dempster, A. P. (1972). Covariance selection. Biometrics, 28, 157-175.


Chapter 5

A BLOCK MULTIFRONTAL SUBSTRUCTUREMETHOD FOR SOLVING LINEAR ALGEBRAICEQUATION SETS IN FE ANALYSIS SOFTWARE

Sergiy Fialko*

Institute of Informatics, Tadeusz Kosciuszko Cracow University ofTechnology, Warszawska str. 24, 31-155 Cracow, Poland

Abstract

A block substructure multifrontal method for solution of large symmetricalsparse equation sets that appear in finite element problems of structural and solidmechanics is proposed for desktop multicore computers. The analysis of astructure's topology reduces the fill-ins and allows the coupling of equations intoblocks. A symmetrical storage scheme for dense matrices and performanceoptimizations provide a combination of an efficient usage of random-accessmemory and a high rate of factorization.

Introduction

The proposed method for solving large sparse symmetrical equation setswhich appear in applications of the finite element method to problems ofstructural mechanics is based on an analysis of the design model topology rather

* E-mail address: [email protected]

Sergiy Fialko94

than on that of the sparse matrix structure itself as it is done for the classicalmultifrontal method [1, 9]. The adjacency graph for nodes of the finite elementmodel together with lists of nodes for each finite element make up the basis forsuch an analysis. The natural coupling of equations into groups made possible bythe existence of several equations per node produces dense matrix blocks in theglobal sparse stiffness matrix and simplifies its decomposition into dense sub-matrices. This entails an increased performance of computation when the BLAS 3level matrix multiplication is used [2].

The Block Substructure Multifrontal Method

The given structure (Figure 1.a, 1.b) is divided into separate finite elementsand then is assembled step-by-step according to a chosen reordering algorithm.The substructures and separate finite elements that contain a node to be eliminatedat a given assembling-elimination step (a fully assembled node) are then coupled(Figure 2). The formed substructure produces a matrix considered as a dense oneand consisting of fully assembled equations associated with the given node and anincomplete part [3–6]. The fully assembled equations are eliminated immediately.Table 1 presents the assembling-eliminating process for the example shown inFigure 1.a. Running through Table 1 from bottom to top makes up a substructure(frontal) tree (Figure 3) which presents the sequence of creation of thesubstructures.

1

2

3 4

5

6

1

2

3

4

5

6

(à)

1 2

3 4

5 6

(b)

Figure 1. An example of a structure (a plane frame (a)) and its corresponding nodaladjacency graph (b). The finite element numbers are shown by figures in circles. Theminimum degree algorithm [7] produces the node elimination ordering 1,6,4,3,5,2. Thefill-in 3-5 is shown as a dash line.

A Block Multifrontal Substructure Method… 95

Table 1. The assembling-eliminating process.

Substructure Number ofeliminated node

List ofnodes

List of previoussubstructures

List of finiteelements

Sstr 1 1 2,1 – 1Sstr 2 6 5,6 – 6Sstr 3 4 5,3,4 – 3,4Sstr 4 3 2,5,3 3 2Sstr 5 5 2,5 4,2 5Sstr 6 2 2 1,5 –

The new substructure numbering (Figure 3) allows one to reduce the storageof incomplete parts of the frontal matrices. The partial factorization procedure is:

⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛TT

ST LW

II

ILWC

DWWC

~0

00

0

~~, (1)

where C is an incomplete part and block row WT, D consists of fully assembledequations. The sign diagonal Is allows us to consider an indefinite matrix too.

Figure 2. The assembling-eliminating process. Each substructure sstr is assembled fromsubstructures created at previous steps and separate finite elements Fe. Fully assemblednodes are eliminated and are marked by crossed digits in circles.

Sergiy Fialko96

1

2

3

4

5

6

1

2

3

4 5

6

Figure 3. The substructure tree. New substructure numbers are shown in italic.

For large-scaled problems, multiple fully assembled nodes occur at someassembling steps. They produce sequential branches in the frontal tree. Thecoupling of corresponding fully assembled equations into large blocks is a veryimportant factor in increasing the performance.

The partial factoring occurs in three stages

TS

TS

TS

T

ST

S

WIWCCWIWCC

WWILW

ILLILD

~~~~~~.3

~~.2

,.1

⋅⋅−=→⋅⋅+=

→⋅⋅=

→⋅⋅=(2)

Stage 1 consists of factoring the diagonal block D. Stage 2 presents thesolution of an equation set with low triangle matrix L and multiple right-handparts. And Stage 3 is the calculation of the Schur complement.

The parallelization on the basis of OpenMP is applied at both Stage 2 andStage 3. The existing Blas 3 level DGEMM, DSYRK procedures from the IntelMKL library and other high performance libraries require the N2 entries to storematrix C even if C is symmetrical.

A special microkernel based on [8] has been developed to perform the fastcomputation of T

S WIWCC ~~~ ⋅⋅−= using a vectorization of operations, XMMregister blocking, cache blocking and packing of data to reduce the readingmisses. This approach maintains a symmetrical storage scheme and requiresapproximately N2/2 entries to store C [6]. It is very important for desktopcomputers which have a very restricted RAM storage.


Numerical Results

A desktop computer Intel® Core™2 Quad CPU Q6600 @2.40 GHz, RAMDDR2 800 MHz 8 GB has been used. A square plate with the mesh 800x800 isunder consideration (3 849 594 equations). The proposed method (BSMFM) iscompared with the sparse direct solver of the ANSYS 11.0 software anddemonstrates an efficient usage of random-access memory (Table 2).

The second test (a cube consisting of brick volumetric finite elements with themesh 50x50x50, Table 3) illustrates a good performance of the BSMFM solver.

Table 2. Duration of numerical factoring, s for plate 800x800.

Number of processorsMethod Platform 1 2 4BSMFM ia32 978 768 670ANSYS 11.0 ia32 Insufficient random-access memory

Table 3. Duration of numerical factoring, s for cube 50x50x50.

Number of processorsMethod Platform 1 2 4BSMFM ia32 827 504 365ANSYS 11.0 ia32 1 610 882 544

Figure 4. Finite element model of a multistory building (3 198 609 equations).

Sergiy Fialko98

The last numerical example (3 198 609 equations, Figure 4) is taken from thecomputational practice of SCAD Soft (www.scadsoft.com). The numericalfactoring time is 1 332 s on four processors. The Intel 64 platform has been used.

Conclusion

The proposed method demonstrates at the same time a good performance andan efficient usage of the random-access memory. It is based on mechanicalconcepts which allow one to produce the efficient coupling of equations intogroups. This improves the rate of numerical factoring. The developed microkernelfor calculation of the Schur complement makes it possible to maintain asymmetrical storage scheme and implement various schemes of high performance.The disk storage is involved when the dimension of a problem is too large to beallocated in RAM.

References

[1] Amestoy, PR; Duff, IS; L’Excellent, JY. Multifrontal parallel distributedsymmetric and unsymmetric solvers. Comput Meth Appl Mech Eng. 2000,184, 501-520.

[2] Demmel, JW. Applied Numerical Linear Algebra; SIAM, Philadelphia,1997, 421.

[3] Fialko, S. Yu. Stress-Strain Analysis of Thin-Walled Shells with MassiveRibs. Int App Mech., 2004, 40, 4, 432-439.

[4] Fialko, S. Yu., A block sparse direct multifrontal solver in SCAD software,Proceedings of the CMM-2005–Computer Methods in Mechanics.Czestochowa, Poland, 73-74, 2005.

[5] Fialko, S. A Sparse Shared-Memory Multifrontal Solver in SCAD Software.Proceedings of the International Multiconference on Computer Science andInformation Technology. October 20-22, 2007, Wisła, Poland, 3 (2008),ISSN 1896-7094, ISBN 978-83-60810-14-9, IEEE Catalog NumberCFP0864E-CDR277-283, 2008. (http://www.proceedings2008.imcsit.org/pliks/47.pdf).

[6] Fialko, S. The direct methods for solution of the linear equation sets inmodern FEM software; SCAD SOFT, Moscow, 2009, 160. (in Russian).

[7] George, A; Liu, JWH. Computer Solution of Large Sparse Positive DefiniteSystems, Prentice-Hall, Inc., Englewood Cliffs, NJ, 1981, 256.


[8] Goto, K; Van De Geijn, R. A. Anatomy of High-Performance MatrixMultiplication. ACM Transactions on Mathematical Software., 2008, 34, 3,1-25.

[9] Gould, NIM; Hu, Y; Scott, JA. A numerical evaluation of sparse directsolvers for the solution of large sparse, symmetric linear systems ofequations. Technical report RAL-TR-2005-005, Rutherford AppletonLaboratory, 2005, 31.

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In: Structural AnalysisEditor: M.L. Camilleri, pp. 101-128

ISBN 978-1-61668-987-2c© 2010 Nova Science Publishers, Inc.

Chapter 6

NON LINEAR STRUCTURAL ANALYSIS.APPLICATION FOR EVALUATING SEISMIC

SAFETY

Juan Carlos Vielmaa,∗, Alex Barbat b and Sergio Oller ba Lisandro Alvarado University

b Technical University of Catalonia

Keywords: Non-linear analysis, ductility, overstrength, seismic safety.

1. Introduction

Performance-Based Design is currently accepted commonly as the most ad-vanced design and evaluation approach. However, successful application of thisprocedure depends largely on the ability to accurately estimate the parametersof structural response.

Determination of these parameters requires application of analysis proce-dures where the main non-linear behavior features (constitutive and geometri-cal) of structures are included. This chapter presents and discusses these fea-tures of non-linear behavior and how they are incorporated in the process ofstatic or dynamic structural analyses. Non-linear analysis leads to determina-tion of significant structural response parameters whenever estimating seismicresponses such as ductility, overstrength, response reduction factor and damage

∗E-mail address: [email protected]

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102 J.C. Vielma, A.H. Barbat and S. Oller

thresholds; being these the main response parameters for evaluating the seismicsafety of structures. In order to illustrate application of the non-linear procedurebeing described, a set of concrete-reinforced moment-resisting framed buildingswith various numbers of levels was selected. These buildings were designed ac-cording to ACI-318 [1] for high and very high level of seismic hazard.

Seismic safety of regular concrete-reinforced framed buildings is studiedusing both the static and dynamic non-linear analyses. Static analysis consistsin using the pushover procedure and dynamic analysis is done by using the in-cremental dynamic analysis (IDA). Analysis was performed using the PLCdcomputer code [2] which allows incorporation of the main characteristics ofreinforcement and confinement provided to the cross sections of structural ele-ments (beams and columns). A set of 16 concrete-reinforced framed buildingswith plane and elevation regularity was designed according to ACI-318 [1] andfor loads prescribed by the ASCE7-05 [3]. Results obtained from static and dy-namic non-linear analyses allowed calculation of global ductility, overstrengthand behavior factors. Behavior factors are compared with design values pre-scribed by the ASCE7-05, in order to verify validity of design values.

Seismic safety of buildings has been evaluated using an objective damage-index obtained from the capacity curve, computed for normalized roof displace-ments corresponding to performance point. Additionally, five damage thresh-olds are defined using the values of inter-story drifts associated with severalLimit States. Damage thresholds lead to obtain fragility curves and damageprobability matrices, used in order to evaluate the seismic safety of the code-designed buildings under study.

2. Seismic Design of Buildings

The main objective of the seismic design is to obtain structures capable ofsustaining a stable response under strong ground motions. Some aspects of thecurrent seismic analysis procedures allow for adapting non-linear features intoan equivalent elastic analysis and, obviously, formulation of these procedures isessential for assuring a satisfactory earthquake-resistant design.

In earthquake-resistant engineering, stable behavior is achieved throughcompliance with conceptual design, thus implying regularity of the structureboth in plane and elevation as well as continuity of resistant elements to lateralloads. It is also essential that the structure elements are able to dissipate energy,reaching damage levels which do not threaten the stability of the structure as a

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Non Linear Structural Analysis 103

Figure 1. Typical reinforcement in beam-column special zones.

whole. In order to achieve this global behavior in concrete-reinforced buildingsit is necessary to supply proportional confinement in special zones of beams andcolumns, finding these zones near to beam-column joints, see Figure 1.

It is especially interesting to know the seismic behavior of code-designedbuildings. In order to study the behavior of low and medium vibration periods, aset of regular concrete-reinforced moment-resisting framed buildings (MRFB)designed according to ACI-318 were analyzed. Low and medium period re-sponses were obtained by considering variable number of stories (3, 6, 9 and12). Structural redundancy was included varying spam number (3, 4, 5 and 6).For each building structure, inner and outer frames were defined to the corre-

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Figure 2. Plan and elevation views of designed buildings.

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sponding load ratio (seismic load/gravity load). Frame members were analyzed,designed and detailed following the code prescriptions for special moment- re-sisting frames (high ductility level). Seismic demand is defined for type B soil(stiff soil) and for a peak ground acceleration of 0.3g and 0.4g. Geometric char-acteristics of typical frames are shown in Figure 2.

Non-linear analysis procedures have been used in previous studies to as-sess the seismic design of buildings designed according to specific design codes[4–6]. Static incremental non-linear analysis (Pushover Analysis) is an analysisprocedure commonly adopted by the scientific community and practicing engi-neers in order to evaluate the seismic capacity of new or existing buildings. Thisanalysis can be performed by using a predefined lateral load distribution; lateralload distribution is usually applied following a specific pattern, which corre-sponds to the shape of lateral displacements obtained from the modal analysis.

Dynamic analysis can be applied using an adequate set of records obtainedfrom strong motion databases or from spectrum-compatible design synthesizedaccelerograms.

2.1. Seismic Response Parameters

The seismic response parameters considered most relevant in recent worksare: global ductility, overstrength and behavior factor which can be calculatedby applying deterministic procedures based on non-linear response of structuressubject to static or dynamic loads. Although it is difficult to find a method todetermine global yield and ultimate displacements [7], a simplified procedure isapplied in this work.

The procedure is based on non-linear static response obtained via finite el-ement techniques, which allows generating idealized bilinear capacity curveshape shown in Figure 3, with a secant segment from the origin to a point thatcorresponds to 75% of maximum base shear [8, 9]. The second segment, repre-senting the branch of plastic behavior was obtained by finding the intersectionof the aforementioned segment with another horizontal segment, correspondingto maximum base shear. Using this compensation procedure guarantees that en-ergies dissipated by the ideal system and by the modeling one, are equal (seeFigure 3).

For a simplified non-linear static analysis, there are two variables that typ-ify the quality of seismic response of buildings. The first is global ductility µ,defined as

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Figure 3. Scheme for determining displacement ductility and overstrength.

µ =∆u

∆y(1)

calculated based on values of yield drift, ∆y, and ultimate drift, ∆u, representedin the idealized capacity curve shown in Figure 3.

Second variable is the overstrength of the building RR, defined as ratio ofyielding base shear, V y to design base shear, V d (see Figure 3).

RR =Vy

Vd(2)

3. Structural Modeling

In order to obtain non-linear responses of buildings, it is necessary to modelthe structures taking into account their geometrical and mechanical specifica-tions. Plane frames are used for static and dynamic analyses. This requiresdefining the different types of frames, mainly depending on the relationshipbetween seismic and gravity loads carried on by the frames. Therefore, threetypes of frames are defined: outer and inner load frames, and bracing frames,see Figure 4.

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Figure 4. Definition of different building frames.

Thus frames are driscretized by taking into consideration the existence ofspecial zones in beams and columns. This requires definition of the elementscovering the length of special confined zones. Figure 5 shows a typical dis-cretization obtained for a three stories frame.

Figure 5. Frame discretization.

4. Non Linear Analysis

Advances made in the field of non-linear structural analysis and develop-ment of improved computational tools have enabled the application of more

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realistic analysis procedures for new and existent buildings, taking into accountthe main features of their seismic non-linear behavior, such as constitutive non-linearity (plasticity and damage) and geometrical non-linearity (large deforma-tions and displacements).

Non-linear incremental static and dynamic analyses are performed usingthe PLCd finite element code [2, 10, 11]. PLCd is a finite element code whichworks with two and three-dimensional solid geometries as well as with pris-matic, reduced to one-dimensional members. By combining both numericalprecision and reasonable computational costs [12,13] it provides a solution andit can deal with kinematics and material non-linearity. To control their evolu-tion, it uses various 3-D constitutive laws to predict material behavior (elastic,visco-elastic, damage, damage-plasticity, etc. [14]) with different yielding sur-faces (Von-Mises, Mohr-Coulomb, improved Mohr-Coulomb, Drucker-Prager,etc. [15]). Newmark’s method [16] is used to perform dynamic analysis. A moredetailed description of the code can be found in Mata et al. [12, 13]. TFor deal-ing with composite materials, the main numerical features included in the codeare: 1) Classical and serial/parallel mixing theory is used to describe the be-havior of composite components [17]. 2) 2) Anisotropy Mapped Space Theoryenables the code to consider materials with a high level of anisotropy, withoutassociated numerical problems [18]. 3) Debonding Fiber-matrix, which reducesthe composite strength due to failure of reinforced-matrix interface, is also con-sidered [19].

Experimental evidence has shown that inelasticity in beam elements can beformulated in terms of cross-sectional quantities [20] and, therefore, beam’s be-havior can be described by using concentrated models, sometimes called plastichinge models, which confine all inelastic behavior at beam ends using ad-hocforce-displacement or moment-curvature relationships [21]. But in the formu-lation used in this computer program, the procedure consists of obtaining theconstitutive relationship at cross-sectional level by integrating a selected num-ber of points corresponding to fibers directed along the beam’s axis [22]. Thus,the general nonlinear constitutive behavior is included in the geometrically exactnonlinear kinematics formulation for beams proposed by Simo [23], consideringan intermediate curved-reference configuration between the straight-referencebeam and the current configuration. To solve the resulting non-linear problem,displacement based method is used. Plane cross-sections remain plane after de-formation of the structure; therefore, no cross sectional warping is considered,avoiding inclusion of additional warping variables in the formulation or iterative

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procedures to obtain corrected cross-sectional strain fields. Thermodynamicallyconsistent constitutive laws are used to describe the material behavior of thesebeam elements, thus allowing obtaining a more rational estimation of the en-ergy dissipated by structures. The simple mixing rule for material compositionis also considered when modeling materials for these elements, composed byseveral simple components. Special attention is paid to obtain the structuraldamage-index capable of describing the structure load-carrying capacity.

According to the Mixing Theory, N different components coexist in a struc-tural element, all of them undergoing the same strain; therefore, strain compati-bility is forced among material components. Free energy density and dissipationof composite are obtained as the weighted sum of free energy densities and dis-sipation of components, respectively. Weighting factors Kq are the participationvolumetric fraction of each compounding substance, Kq = Vq

V , obtained as thequotient between the q−th component volume, Vq, and total volume, V [10–13].

Discretization of frames was performed using finite elements whose lengthsvary depending on column and beam zones with special confinement require-ments. These zones are located near the nodes where maximum seismic demandis expected, and are designed according to general dimensions of structural el-ements, diameters of longitudinal steel, span length and storey heights. Frameelements are separated into equal thickness layers with different composite ma-terials, characterized by their longitudinal and transversal reinforcement ratio(see Figure 6). Transverse reinforcement benefits are included by using the pro-cedure proposed by Mander et al. [24]. This procedure consists of improvingthe concrete compressive strength depending on quantity and quality of the lon-gitudinal and transversal reinforcement.

4.1. Non Linear Static Analysis

In order to evaluate inelastic response of structures, pushover analysis wasperformed applying a set of lateral forces corresponding to seismic actions of thefirst vibration mode. Lateral forces were gradually increased starting from zero;passing through the value inducing transition from elastic to plastic behavior andfinally reaching the value corresponding to ultimate drift (i.e. point at which thestructure can no longer sustain any additional load and collapses). Before thestructure is subject to lateral loads simulating a seismic action, it is first subjectto the action of gravity loads, lumped in the nodes defined by the beam-columnsjoints, in concurrence with combinations applied in the elastic analysis. The

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Figure 6. Discretization of RC frame elements.

method applied does not allow for evaluation of torsion effects, being the modelused a 2D one. Capacity curves obtained in the analysis are shown in Figure 7.

Figure 7. Capacity curves of the studied buildings.

Non-linear static analysis calculates cumulative damage in structural el-ements by using the procedures described in Section 4.3..Results of localdamage-index at collapse displacement calculated for two of the buildings understudy are shown in Figure 8. In this figure, each rectangle represents the magni-

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tude of damage reached by the element. It is important to observe that for lowrise buildings (N=3) the maximum values of damage correspond to the elementslocated at both ends of the first storey columns; this damage concentration cor-responds to a soft-storey mechanism. Instead, high rise buildings (N=6, 9 and12) show their maximum damage values at low level beam ends, according tothe desired objective of conceptual design which is to produce structures withweak beams and strong columns.

(a) 3 stories building (b) 9 stories building

Figure 8. Distribution of local damage-index at collapse displacement.

Figure 9 shows overstrength computed values for outer frames of the build-ings under study, plotted in function of number of stories. Results clearlydemonstrate that the influence of number of spans, equivalent to consideringdifferent numbers of resistant lines, is very low. In all cases, overstrength com-puted values are closer to each other. It can also be seen that these values ofcombined overstrength factors and redundancy are slightly lower than the valueprescribed by ASCE-7 for design of ductile-framed buildings.

4.2. Non Linear Dynamic Analysis

In order to evaluate the dynamic response of buildings, the IDA (Incremen-tal Dynamic Analysis) procedure was applied. This procedure consists in per-forming time-history analysis for registered ground motions or for artificiallysynthesized accelerograms scaled in such a way of inducing increasing levels ofinelasticity in each new analysis [25]. A set of six artificial accelerograms, com-

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Figure 9. Overstrength and redundancy vs. number of stories.

patible with B type soil of the ASCE-7 elastic design spectrum, was generated,see Figure 10.

Figure 10. Synthesized accelerograms compatible with the ASCE-7 elastic de-sign spectrum.

Figure 11 shows the elastic design spectrum and the 5% damping responsespectra computed from the set of artificial accelerograms for the two levels ofseismic hazard (0.3g and 0.4g) used in elastic design of buildings.

Peak acceleration equal to basic design acceleration is assumed in the anal-ysis. Record is scaled from this value until a plastic response is reached bythe structure; this procedure continues on and on until achieving collapse dis-placement. A maximum value of structural response is calculated for each valueof scaled acceleration. IDA curves are obtained by plotting the earthquake peak

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(a) 30%g (b) 40%g

Figure 11. Elastic design spectrum and response spectra.

acceleration in function of maximum value of the computed structural response.Collapse is reached when the capacity of the structure drops [9,26,27]. A usualcriterion is to consider that collapse occurs whenever the slope of the curve isless than 20% of the elastic slope [25, 28]. Figure 12 shows IDA curves com-puted for the 3-span outer frame of the 3 storey building. Note that the collapsepoints of frames are closer to the values of the capacity curves.

Figure 12. Set of IDA curves and capacity curve.

The dynamic analysis is useful to assess behavior factors q of the buildings.For this purpose the following equation has been proposed [4]:

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Table 1. Computed behavior factors of outer frames (0.3g)

Number of storeys qequation qcodeqequation

qcode

3 19.70 8.00 2.466 16.45 8.00 2.059 15.46 8.00 1.9312 16.09 8.00 2.01

q =ag(Collapse)

ag(Designyield)(3)

where ag(Collapse) and ag(Designyield) are collapse and yielding design peak groundacceleration, respectively. The former is obtained from IDA curves and thelatter is calculated from elastic analysis of the building. Average values of theq computed behavior factor of the buildings under study are shown in Table 1;these values correspond to the dynamic response obtained for the set of tensynthesized accelerograms, and are compared to behavior factors prescribed bythe design codes.

Computed behavior factors show that, regardless of building height, seis-mic design performed by using the ACI318 leads to structures with satisfactorylateral capacity whenever subjected to strong motions.

4.3. Objective Damage Index

Some indexes measure the global seismic damage of a structure from itslocal damage, i.e. the contribution in a given instant of cumulative damage instructural elements to the structure being subject to seismic demand. Among theindexes which have served as baseline for many researches, it can be mentionedthe one proposed by Park and Ang [29] which can determine damage in anelement, based on non-linear dynamic response by the following expression:

DIe =δm

δu+

βδuPy

∫dEh (4)

where δm is the maximum displacement, δu is the ultimate displacement, β isa parameter adjusted depending on materials and structural type, Py is the yield

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strength and∫

dEh is dissipated hysteretic energy. This damage-index is validfor an element at a local level; however, it is possible to apply this index forcalculating the values for a specific structural level, or for the whole structure.

Another damage-index based on stiffness degradation is proposed by Guptaet al. [30]. They have formulated an expression based on the relationship be-tween ultimate and yielding displacements, equivalent to ultimate and yieldingstiffness. This formulation also includes a design ductility value according to:

DI =xmazz00−1

µ−1(5)

A local damage-index is calculated using the PLCd finite element programwith a constitutive damage and plasticity model enabling the correlation of dam-age with lateral displacements [16, 31]

D = 1− ‖Pin‖‖Pin

0 ‖(6)

where ‖Pin‖ and ‖Pin0 ‖ are the norm of current and elastic values of the internal

forces vectors, respectively. Initially, the material remains elastic and D = 0, butwhen all the energy of the material has been dissipated ‖Pin‖→ 0 and D = 1.

Figure 13. Parameters for determination of damage-index.

It is important to know the level of damage reached by a structure subject tocertain demand. This is possible if the damage-index is normalized with respect

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to the maximum damage which can occur in the structure [32]. This objectivedamage-index 0≤ D≤ 1 achieved by a structure at any P is defined as

DPob j =

DP

DC= DP

µ1−µ

=(1− KP

K0)µ

1−µ(7)

For example, for P point, which might be the performance point resultingfrom the intersection between inelastic demand spectrum and capacity curve(obtained from pushover analysis), it corresponds a stiffness KP. Other parame-ters are initial stiffness K0 and ductility µ, calculated by using yielding displace-ment ∆∗y corresponding to the intersection of initial stiffness with maximumshear value (see Figure 13).

Objective damage-index is computed using Eq. 7, from the non-linear staticanalysis. Figure 14 shows evolution of objective damage-index respecting thenormalized roof drift, computed for all frames of the 3 stories building. Curvesare similar to those obtained for frames of the same number of stories.

Figure 14. Evolution of damage-index of the 3 stories building.

5. Seismic Safety

Nowadays, it is widely accepted among the scientific community that thePerformance-Based Design is the most rational procedure. This requires defini-tion of a set of Limit States in order to evaluate the damage that may be causedby earthquakes. These Limit States are frequently defined by engineering de-mand parameters, among which the most used are inter-story drift, global drift

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and global structural damage. These demand parameters define damage thresh-olds associated with Limit States, which allows calculating fragility curves anddamage probability matrices used in seismic safety assessment of buildings.

Consequently, it is necessary to select the evaluation criterion which repre-sents the moment when the structure reaches a specific limit state. According tothe above, interstory drift is a dimensionless value which quantifies properly thedamage under lateral loads. Among published values, a set of inter-story driftswere selected from which specific damage reaches a threshold corresponding toa Limit State.

Damage thresholds are determined using the VISION 2000 procedure [33],in which they are expressed in function of interstory drifts. In this chapter, fivedamage state thresholds are defined both from interstory drift curve and fromcapacity curve. For the slight damage state, roof drift corresponding to an inter-story drift of 0.5% is considered. Service damage state corresponds to the roofdrift for which an interstory drift of 1% is reached in almost all the structurestories. Repairable damage state is defined by an inter-story drift of 1.5%. Asevere damage state is identified by a roof drift producing a 2.0% of interstorydrift at each level of the structure. Finally, a total damage state (collapse) cor-responds to ultimate roof displacement obtained from the capacity curve. Meanvalues and standard deviation were computed from the non-linear response ofbuildings with the same geometric and structural type, with a variation of thenumber of spans from 3 to 6 [34, 35].

Figure 15. Determination of the damage thresholds.

To determine damage thresholds it is necessary to plot the evolution of inter-story drifts with respect to global drift (roof displacement normalized with totalbuilding height). With this plot it is possible to obtain the global drift limitcorresponding to a state i, characterized by interstory drift, see Figure 15. In

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the case of a building with n levels, n evolution curves are obtained; globaldrift of a Limit State corresponding to the intersection of the first curve with theinter-story drift characterizing the Limit State.

Figure 16 Figure 16 shows the results obtained from outer frames of the 6storey buildings designed for an acceleration of 0.3g. This figure shows thatthere is a clear dispersion of results for displacement at collapse, but these arekept within a range between 2.25% and 2.5%, compared to the values reportedby Kircher et al. [36] and Dymiotis et al. [37], which are between 2% and 4%.

Figure 16. Mean and standard deviation for damage thresholds.

Figure 17 shows a comparison of results obtained by the above procedurewith experimental results reported by Dymiotis et al. [37]. It can be seen thatvalues obtained from non-linear analysis fit quite well with experimental values,regardless of the number of spans being considered.

Given this difference in results, Vielma et al. [34] have proposed the follow-ing expressions to determine interstory drifts δ (expressed in%) from normal-ized roof drifts of the buildings (∆/H expressed in%)):

δ = 0.1299+0.4358(∆H

) f orN = 3

δ = 0.1503+0.5256(∆H

) f orN = 6

δ = 0.06518+0.6280(∆H

) f orN = 9

δ = 0.01184+0.6312(∆H

) f orN = 12

(8)

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Figure 17. Comparison of numerical and experimental results.

Figure 18 shows damage thresholds values applied to the capacity curveof the outer frame of the 3 level building, designed for acceleration of 0.3g.By using Equation 7, objective damage-indexes are calculated with thresholdsassociated to Limit States.

The combined use of thresholds and damage-indexes allows a quick charac-terization of the seismic response of a building and provides sufficient criteriafor evaluating the behavior of a particular configuration or pre-design subject tospecific demand buildings, e.g. the spectrum prescribed by the design code.

5.1. Performance Point

In order to evaluate seismic safety of the buildings, the performance pointrepresents an adequate measure. It is obtained by maximum drift of an equiv-alent single degree of freedom induced by the seismic demand. The points ofall cases being studied have been determined by using the N2 procedure [38]which requires transformation of the capacity curve into a capacity spectrum,expressed in terms of spectral displacement, Sd and spectral acceleration, Sa.The former is obtained by means of equation

Sd =δc

MPF(9)

where δc is the roof displacement. The term MPF term is the modal participa-tion factor calculated from the response in the first mode of vibration.

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Figure 18. Damage thresholds with associated damage-index values.

MPF = ∑ni=1 miφ1,i

∑ni=1 miφ2

1,i(10)

Spectral acceleration Sa is calculated by means of:

Sa =VW

α(11)

where V is the base shear, W is the seismic weight and α is a coefficient obtainedas

α =(∑n

i=1 miφ1,i)2

∑ni=1 miφ2

1,i(12)

Figure 19 shows a typical capacity spectra crossed with the corresponding elas-tic demand spectrum. Idealized bilinear shape of the capacity spectra is alsoshown.

Spectral displacement values corresponding to performance point are shownin Table 2. An important feature influencing the non-linear response of buildingsis the ratio between performance point displacement and collapse displacement.This ratio indicates whether the behavior of a structure is ductile or fragile.Lower values correspond to the 12-storey buildings, which have a weak-beamstrong-column failure mechanism.

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Figure 19. Determination of performance point.

Table 2. Roof drift of performance points for the studied buildings

Normalized roof drift RatioNumberof sto-ries

Performancepoint (%)

Staticanalysis

Dynamicanalysis(average)

Staticanalysis

Dynamicanalysis(average)

3 0.71 2.93 2.52 0.24 0.286 0.47 2.41 2.65 0.20 0.189 0.44 2.58 2.67 0.17 0.1712 0.28 2.49 2.70 0.11 0.10

5.2. Fragility Curves

Fragility curves are particularly useful for evaluating seismic safety of build-ings. They are obtained by using spectral displacements determined for damagethresholds and considering a lognormal probability density function for spectraldisplacements which define damage states [39–42].

F(Sd) =1

βdsSdn√

2πexp[−1

2(

1βds

lnSd

S̄d,ds)2] (13)

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where S̄d,ds is the mean value of spectral displacement for which the buildingreaches damage state threshold ds and βds is the standard deviation of the nat-ural logarithm of spectral displacement for damage state ds. The conditionalprobability P(Sd) of reaching or exceeding a particular damage state ds, giventhe spectral displacement Sd , is defined as

P(Sd) =∫ S

0F(Sd)dSd (14)

With fragility curves it is possible to calculate the values of probability ofexceeding a particular limit state. Probabilities are calculated for a specific dis-placement or acceleration, usually obtained from a level of demand. Demandgenerally corresponds to the point of performance described in the previoussubsection. Figure 20 shows fragility curves calculated for inner frames of the3 and 12 storey buildings.

For more complete results the reader is referred to [27, 34, 35]

(a) Inner frame of the 3 stories building (b) Inner frame of the 12 stories building

Figure 20. Fragility curves with performance point displacement.

Figure 21 shows damage probability matrices calculated for performancepoints achieved for inner frames of the 3 and 12 storey buildings. It is importantto note that for frames of the same building, probabilities vary according to loadratio (seismic load/gravity load). Another important feature is the increasingvalues of probabilities that low rise buildings reach higher damage states; asdiscussed in previous sections, collapse of these buildings is associated with thesoft-storey mechanism. For example, in the case of inner frames of the 3-levelbuilding, probability to reach collapse is four times higher than in the case of theouter frame of the same building. In contrast, 6, 9 and 12 storey buildings showvery low probabilities to reach higher damage states, regardless of load ratio and

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span number. For these buildings, predominant damage states are non-damageand slight damage.

0

0.1

0.2

0.3

0.4

0.5

No damage

3S

6S

9S

12S

Slight

Repairable

Extended

Stability

Collapse

Figure 21. Damage probability matrices of outer frames.

Damage probability matrices contain the cumulative probability of reachinga specific limit state. This allows a qualitative assess of structural responseduring a specific seismic action. In Figure 21 it is possible to appreciate theprobability matrices of internal frames, highlighting that frames of buildingswith 3 levels have a probability of reaching more advanced stages of damagecompared to frames of buildings of 6, 9 and 12 levels. This feature is repeatedregardless of number of spans and location of frame. The difference in theresponse of low buildings is due to the fact that the failure mechanism of these

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buildings is the soft story mechanism, in which damage is concentrated at theends of columns in ground level, resulting in loss of global stability.

5.3. Concluding Remarks

Steps in this chapter show the importance of non-linear analysis in the eval-uation of seismic safety of buildings. By incorporating the characteristics of theconstitutive non-linearity of materials (plasticity and damage) and geometric(large deformations and displacements) of the structure, it is possible to esti-mate adequately the design parameters prescribed in codes.

These parameters are applied based on the experience of scientists and en-gineers; therefore, their validation helps to improve understanding the behaviorof structures subject to earthquakes.

In order to apply the non-linear analysis in the evaluation of buildings de-signed according to current earthquake-resistant codes (ACI-318, ASCE-7), agroup of concrete-reinforced buildings have been selected. These buildingshave been studied by applying static and dynamic procedures which have al-lowed calculating ductility and overstrength values and response-reduction fac-tors. Overall, assessment has enabled the awareness that design parameters areadjusted appropriately to safety requirements.

In the study, all parameters except overstrength values are higher than thoseprescribed by the design code. Computed values are slightly lower than thoseprescribed in the ASCE-7. Overstrength values are often interpreted as safetyfactors by some designers.

Among the assessment procedures explained, one is the verification of theappropriateness of applying the objective damage-index as a tool to quicklyevaluate overall performance of structures when they are subject to a specificseismic demand.

Analyses applied demonstrated that although buildings are designed to ap-ply the same special requirements to ensure adequate seismic performance, thesafety exhibited by these buildings is not the same. This can be verified by ob-serving the fragility curves obtained for buildings of 3 and 12 levels, where thefirst are more likely to reach advanced stages of damage. This feature is due tothe failure mode characteristic of low buildings that corresponds to a soft-storymechanism.

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References

[1] ACI, Building code requirements for structural concrete, (ACI318-05).Detroit: American concrete institute, 1st ed., 2005.

[2] PLCd, Non-linear thermo mechanic finite element oriented to PhD studenteducation. Barcelona: CIMNE, 1st ed., 2008.

[3] ASCE, Minimum Design Loads for Buildings and Other Structures. Re-ston: American Society of Civil Engineers, 1st ed., 2005.

[4] A. M. Mwafi and A. Elnashai, “Calibration of force reduction factors ofRC buildings,” J Earthq Eng, vol. 6(2), pp. 239–273, 2002.

[5] A. M. Mwafi and A. Elnashai, “Overstrength and force reduction factorsof multistory reinforced-concrete buildings,” Struct des tall buil, vol. 11,pp. 329–351, 2002.

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[11] E. Car, S. Oller, and E. Oñate, “A large strain plasticity for anisotropicmaterials: Composite material application,” Int J Plasticity, vol. 17(11),pp. 1437–1463, 2001.

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[12] P. Mata, S. Oller, and A. H.Barbat, “Static analysis of beam structuresunder nonlinear geometric and constitutive behaviour,” Comput MethodsAppl Mech Engrg, vol. 196, pp. 4458–4478, 2007.

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INDEX

A

acetone, 60 acetylcholine, 15 acid, 42, 43, 44, 45 acne, 38, 40 adaptation, 41 adhesion, 65 aesthetics, 16, 19 AGFI, 87 alkaloids, 42, 43, 44 amnesia, 8 amygdala, 5, 14, 15, 17, 18, 19 anemia, 42 anisotropy, 108 anorexia, 42 anticoagulant, 44 aquatic systems, 49 Aristotle, 16 articulation, 5, 18 assessment, 117, 124 assessment procedures, 124 ataxia, 42 atomic force, viii, 51, 52, 63, 64 atomic force microscope, viii, 51, 52, 53, 63,

64, 65, 69 atoms, 53, 67, 68

B

bacteria, 24 banks, 26, 35 basal ganglia, 5, 7, 8, 9, 10, 11, 17, 18, 19, 20 beams, 35, 54, 56, 102, 103, 107, 108, 111

behavior, vii, ix, 8, 11, 33, 48, 52, 53, 68, 101, 102, 103, 105, 108, 109, 113, 114, 119, 120, 124

binding, 67 binding energy, 67 biodiversity, 34 biomass, 47 biotic, 24, 25, 46 birds, 24 bleeding, 42 blocks, viii, 93, 94, 96 blood, 43 bonding, viii, 52, 64, 69 bonds, 40 brain, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,

16, 17, 18, 19, 20 brain damage, 14 brain functions, 13 brain stem, 5, 8 brain structure, 1 bronchitis, 39, 41

C

calibration, 125 candidates, 3 capillary, 64 carbon, 24, 34, 46, 47, 57, 60 cardiac glycoside, 42 catalysis, 52 category a, 5 cattle, vii, 21, 22, 34, 41, 42 causal relationship, 82 causality, 78 cell, 53

Index

130

central nervous system, 43, 44 cerebellum, 5, 7, 8 cerebral cortex, 8, 11 childhood, 5, 14 children, 14 classical conditioning, 5, 19 classification, 23 cleavage, 54 climate change, 41 clusters, 11, 27 CO2, 41 codes, 105, 114, 124 coffee, 36 cognitive deficit, 9 cognitive deficits, 9 cognitive function, 10, 18 cognitive map, 10 coma, 43, 44, 45 community, 25, 26, 27, 29, 32, 34, 105, 116 compatibility, 109 compensation, 105 competition, 4, 11, 13, 17, 74 competitive advantage, 33 competitiveness, 74 compilation, vii complement, 96, 98 complexity, 47 compliance, 102 components, 5, 26, 28, 32, 50, 68, 108, 109 composition, viii, 33, 52, 53, 63, 66, 67, 68,

69, 109 compounds, 37, 42 computation, 94, 96 concentration, 41, 111 conditioning, 15, 19 configuration, 55, 67, 68, 108, 119 confinement, 102, 103, 109 conscious knowledge, 12 conservation, 41, 46 constipation, 39 construction, 11 contamination, 67 control, 7, 13, 39, 108 cooling, 16 copper, 60 correlation, 9, 17, 77, 78, 80, 81, 82, 84, 85,

91, 115 correlation coefficient, 77, 78, 81, 82, 84, 85 correlations, 84 cortical neurons, 11

coupling, viii, 93, 94, 96, 98 covering, 32, 33, 34, 107 CPU, 97 creative thinking, 18 crystal structure, viii, 51, 54, 55 crystalline, viii, 52, 53, 62, 63 crystallinity, 55 crystals, 53, 54 cycles, 46 cycling, 37, 38

D

damping, 112 decay, 16, 19, 44 declarative memory, 1, 5, 6, 7, 8, 9, 12, 13,

14, 15, 17, 18, 19, 20 decomposition, 94 decoupling, 46 definition, 107, 116 deformation, 108 degradation, 41, 115, 127 density, 22, 41, 60 deposition, 55 depression, 15, 42 derivatives, 43 designers, 124 destruction, 22 diamonds, 9 diarrhea, 40, 42, 43, 44, 45 diffraction, viii, 52, 53, 54, 55, 63, 68 diodes, 57 directors, 2 discretization, 107 discrimination, 10, 13, 14 dispersion, 118 displacement, 106, 108, 110, 111, 112, 114,

116, 117, 118, 119, 120, 122 dissatisfaction, 75, 80 distribution, viii, 25, 26, 32, 52, 65, 68, 105 diversity, 22, 25, 34, 37 dominance, 17, 27, 32, 47 dopamine, 11, 15 drainage, 27 drawing, 1, 2, 6, 7 ductility, ix, 101, 102, 105, 106, 115, 116,

124, 125, 127 duration, 26, 97 dynamic loads, 105

Index

131

E

earth, 41, 52 ecological restoration, 47 ecology, 48, 49 ecosystem, 23, 37, 38, 41, 47, 50 educational system, 76 electromagnetic, 60, 62 electromagnetic waves, 62 electron, viii, 51, 52, 56, 57, 59, 60, 62, 67, 68 electron diffraction, 52 electron microscopy, viii, 51, 56, 59 electronic structure, 62 electrons, 53, 56, 57, 59, 60, 66, 67, 68 emission, viii, 11, 56, 66 emitters, 57 emotion, 17, 74 emotional experience, 15 emotionality, 19 emotions, 15 employees, viii, 73, 74, 75, 76, 77, 83, 90 emulsifying agents, 42 encoding, 14, 15 energy, viii, 24, 51, 52, 56, 57, 67, 68, 74,

102, 109, 115 energy density, 109 environment, 74 environmental factors, 24 episodic memory, 5, 6, 7, 12 epistemology, 16 equilibrium, 50 erosion, 22, 38, 47 estimating, ix, 101 etching, 67 evolution, 20, 34, 108, 116, 117, 118 explicit knowledge, 12, 17, 18 explicit memory, 6, 15

F

factor analysis, 78, 80, 81, 82, 84 fauna, vii, 21, 33, 49 fibers, 35, 46, 108 field emission scanning electron microscopy,

51 films, 52 finite element method, 93, 98 flavonoids, 42, 43

flavor, 3 flight, 4 flora, vii, 21, 30, 34, 36, 38, 49 fluctuations, 37 fluid, 17 focusing, 60, 81 forests, 23, 33 fragility, 102, 117, 122, 124 free energy, 109 friction, 63 frontal lobe, 12 fruits, 36, 37, 39, 40, 42, 43, 44, 45 fuel, 24, 37 full width half maximum, 55 fungi, 24

G

gases, 67 geology, 57 GFI, 87 globalization, 74 globus, 8 glucoside, 43 glutamate, 15 glycoside, 45 gracilis, 27, 37, 38 graph, 94 grass, 22, 25 grasses, 22, 24, 41 gravity, 105, 106, 109, 122 grazing, viii, 27, 30, 34, 41, 46, 49, 52, 55, 68 groups, 22, 24, 33, 37, 94, 98 growth, 47, 59, 62, 64, 75, 76, 77, 85

H

habitat, 24, 33 halophyte, 27 healing, 37 heat, 67 heating, 67 height, 63, 114, 117 hematuria, 42 hemorrhage, 41 heterogeneous systems, 12 high resolution transmission electron

microscope, viii, 52, 62, 68

Index

132

hippocampus, 7, 15, 18, 19 hologram, 16 human actions, 38 human brain, 5, 20 hydrogen, 42 hydrogen cyanide, 42 hygiene, 75 hypothesis, 3, 37, 41

I

image, 1, 2, 6, 56, 57, 58, 59, 60, 61, 63, 64 images, 17, 56, 57, 58, 59, 61, 62, 64, 65 implementation, 22 implicit knowledge, 11, 17 implicit memory, 8, 14, 15 impurities, 54 incidence, viii, 52, 54, 55, 68 inclusion, 108 income, 76, 84, 85 indicators, 46 indirect effect, 82 inelastic, 108, 109, 116 inflammation, 44 ingestion, 44 injuries, 42 instability, 19 instruments, 35, 52, 67, 68 insulators, 52 interaction, 5 interactions, 62 interface, 108 interference, 53 interview, 75 irradiation, 66 irritability, 44

J

joints, 16, 103, 109

K

kidney, 39, 40, 42, 43

L

labor, 90 land, 27, 41, 47, 49 landscape, 46, 48 land-use, 38, 47 language, 6, 7, 8, 13, 16, 18, 19 lattice parameters, 62 laws, vii, 108, 109 leaching, 67 learning, 5, 7, 8, 9, 10, 11, 12, 13, 14, 17, 19 learning behavior, 7 lecithin, 42 lens, 68 leprosy, 40 lesions, 7, 10, 11 lifetime, 1 line, 6, 94 linear systems, 99 linkage, 57 listening, 2 liver, 42, 43 liver damage, 42 livestock, 41, 43, 46 locus, 15 LTD, 15

M

magnesium, 63 magnetic field, 68 magnetic resonance, 11 magnetic resonance imaging, 11 magnetism, 63 malaria, 40 management, vii, 21, 22, 33, 34, 41, 46, 47,

48, 50, 74, 90 mandatory retirement, 74 market, 90 materials science, 53, 57 mathematics, vii matrix, 78, 94, 95, 96 maze tasks, 10 measurement, 55 measures, 53, 74 mediation, 24 memorizing, 2, 13

Index

133

memory, viii, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 93, 97, 98

memory formation, 11 mental processes, 5 metals, 52 metaphor, 3, 14 microclimate, 46 microscope, 52, 56, 57, 59, 60, 63, 68 microscopy, viii, 51, 52, 56, 62, 63, 64 mixing, 108, 109, 126 model, 50, 76, 77, 78, 80, 81, 82, 83, 85, 86,

87, 88, 89, 93, 94, 97, 106, 110, 115, 125, 126, 127

modeling, viii, 49, 73, 77, 78, 81, 82, 84, 85, 105, 109

models, 47, 77, 88, 108 moisture, 23, 26, 27 moisture content, 26 molecules, 57 morphology, vii, 51, 53, 57 motion, 105 motivation, vii, viii, 73, 74, 75, 76, 77, 78, 80,

81, 82, 83, 84, 85, 87, 88, 89, 90 motor control, 8 mountains, 22 mucous membrane, 42, 45 mucous membranes, 42, 45 multiplication, 94 mumps, 38 muscle atrophy, 44

N

nanomaterials, 68 nanometer, 63 nanometers, 52, 63 nanorods, 55, 62, 65 nanoscale materials, 52 nanostructures, viii, 51, 52, 53, 54, 56, 57, 60,

66, 68, 69 nanotechnology, 52, 57 nanowires, 59 narcotic, 42 natural resources, vii, 21 negotiating, 18 neocortex, 8 neural network, 8 neuroimaging, 11 neurons, 8, 11

neuropsychology, 1, 5 neuroscience, 20 neurotransmitter, 11 nicotine, 45 nitrogen, 46 Nobel Prize, 71 nodes, 94, 95, 96, 109 nuclei, 64 nucleus, 8, 11, 12 Nuevo León, 25, 38, 47, 49 nutrients, 23

O

objectives, viii, 73, 74 objectivity, 3, 4 oils, 44 open spaces, 27 operator, 60 order, viii, ix, 13, 52, 57, 60, 63, 73, 76, 77,

90, 102, 103, 105, 106, 109, 111, 116, 119, 124

organ, 11 organic matter, 23, 24 oxidation, 67 oxides, 53 oxygen, 63

P

paints, 4 parallelization, 96 paralysis, 43, 44 parameter, 54, 114 parameters, ix, 101, 102, 105, 116, 117, 124 parents, 14 pasture, 22 pathways, 10 perceptual learning, 19 PET, 11, 64, 65 philosophers, 4 photoelectron spectroscopy, viii, 51 photons, 67 photosynthesis, 24 physical activity, 17 physical properties, 52 physical treatments, 67 physical well-being, 3

Index

134

physics, 52, 58, 61 piano, 5 Picasso, 2 plants, 22, 24, 27, 32, 37, 41, 42 plasticity, 108, 115, 124, 125 platinum, 57 Plato, 14 plausibility, 4 polymer, 126 positive feedback, 49 positron, 11 precipitation, 23 prediction, 9, 17, 127 priming, 7 probability, 9, 102, 117, 121, 122, 123 probability density function, 121 probe, 53, 57, 63, 65 procedural knowledge, 16 procedural memory, 5, 7, 8, 13, 14, 18 producers, 49 production, 17, 18, 35, 47 productivity, 23, 37, 74 program, 108, 115 prostration, 42, 44 proteins, 45 psychologist, 4 psychology, 4 pumps, 68 purity, 54, 68

R

radiation, 54 radius, 63 range, 52, 55, 118 rangeland, 48 reaction time, 9, 12 recall, 1, 2, 16, 17 recognition, 18 recollection, 7 recovery, 49 recovery processes, 49 recreation, 19 red blood cells, 42 redundancy, 103, 111, 112 reflection, 1 region, 5, 7, 10, 12, 18, 19, 23, 47 reinforcement, 102, 103, 109

relationship, 6, 10, 11, 15, 17, 18, 76, 77, 78, 80, 81, 84, 85, 106, 108, 115

reliability, 128 repetition priming, 5 resins, 43, 44 resolution, 52, 56, 59, 62, 63, 68 resource management, 46 resources, 23, 24, 33, 34, 46, 49 respiratory, 42, 44 respiratory problems, 42 respiratory rate, 44 retention, 13, 24 retirement, 90 rubber, 35 runoff, 23

S

safety, vii, ix, 101, 102, 117, 119, 121, 124, 127

salinity, 26 salts, 27 saponin, 42 satisfaction, 75, 76, 77, 78, 80, 82, 83, 88 scabies, 40 scanning electron microscopy, viii scattering, viii, 52, 68 sedative, 43, 44 seed, 42 selected area electron diffraction, viii, 62, 68 self-confidence, 3 semantic memory, 5, 7, 14 semiconductors, 52 sensation, 45 sensors, 52 shape, viii, 52, 53, 68, 105, 120 shear, 105, 106, 116, 120 shrubland, 47 shrubs, 22, 24, 25, 26, 28 signals, 11, 12, 15 silicon, 59, 63 skills, 7, 9 skin, 42, 44, 45 soil, 23, 24, 26, 27, 33, 34, 46, 48, 49, 50,

105, 112 soil erosion, 23 solid state, 57 solvation, 64 spatial memory, 10

Index

135

species, 25, 26, 27, 28, 29, 32, 33, 34, 37, 38, 41, 46, 47, 48

specific surface, 67 spectroscopy, viii, 51, 52, 62 spectrum, vii, 54, 55, 63, 66, 67, 68, 112, 113,

116, 119, 120, 128 speed, 76 stability, 24, 33, 57, 102, 124 standard deviation, 117, 118, 122 steel, 68, 109, 125 stimulus, 11, 14, 15 storage, viii, 6, 24, 93, 95, 96, 98 strain, 109, 125, 127 strategies, 46, 74 strength, 40, 108, 109, 115 striatum, 7, 8, 9, 10, 11 substrates, 64, 69 surface chemistry, 67 symbols, 18 symptoms, 42 synaptic strength, 18 synaptic transmission, 11 syndrome, 7 synthesis, 52

T

tachypnea, 42 tannins, 37, 43, 44 temperature, 23, 41 temporal lobe, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17 terraces, 26 thin films, 55 threshold, 117, 122 thresholds, ix, 41, 46, 102, 117, 118, 119, 120,

121 topology, viii, 93 torsion, 110 toxic effect, 42 toxic substances, 42 toxicity, 45 training, 2, 4 transformation, 19, 119 transgression, 34 transition, 15, 27, 37, 38, 49, 109 transitions, 19, 41 transmission, viii, 51, 52, 59, 60, 62, 68 transmission electron microscopy, viii, 51, 52,

53, 59, 60, 61, 62, 63, 68

transport, 47 transportation, 35, 41 trauma, 16 trees, 3, 22, 24, 25, 26, 28, 29 trial, 9 tumor, 37

U

UNESCO, 23 uniform, 59, 65 United States, 22, 23, 41 urban areas, 128 UV, 67 UV light, 67

V

vacuum, 57, 59, 66, 68 validation, 124 variables, 77, 78, 80, 81, 84, 85, 86, 105, 108 vegetation, vii, 21, 22, 24, 25, 26, 27, 28, 29,

32, 33, 41, 47 vibration, 103, 109, 119

W

wavelengths, 53 woodland, 46 working conditions, 75, 76, 85 working hours, 76, 86

X

XPS, 66, 67, 68 X-ray diffraction, viii, 51, 52, 53, 54, 55, 68 x-rays, 55

Z

ZnO, viii, 52, 55, 56, 57, 58, 59, 61, 62, 64, 65, 68, 69

ZnO nanorods, 55, 59, 64, 65 ZnO nanostructures, viii, 52, 55, 65

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